Network Image Coding for Multicast

Size: px
Start display at page:

Download "Network Image Coding for Multicast"

Transcription

1 Network Image Coding for Multicast David Varodayan, David Chen and Bernd Girod Information Systems Laboratory, Stanford University Stanford, California, USA {varodayan, dmchen, Abstract We consider a new problem in network image coding for multicast. In a multihop mesh network, structured as a directed graph, all nodes decode and display reconstructions of the image (at possibly different qualities). Each node may also perform transcoding before transmitting data downstream in the network. The problem is the design of the coding and transcoding schemes to deliver the best image quality over the network. For a network with diamond topology, we show that multiple description coding combined with Wyner-Ziv transcoding is often superior to other methods. We argue further that the benefits are magnified for larger networks containing one or more diamond subnets. Our image coding experiments demonstrate that multiple description coding with Wyner-Ziv transcoding outperforms single description coding or multiple description coding with conventional transcoding, for both a diamond network and a two-hop mesh network with four branches. R S N 1 N 2 N k R 1 R R2 T Fig. 1. Two-hop mesh network with source node S and viewers at intermediate nodes N i (for 1 i k) and terminal node T. Link capacities are labeled in bits per pixel (bpp) of the original image. R Rk I. INTRODUCTION Consider a multihop mesh network as a directed graph, where each edge is labeled with its capacity (the maximum permitted bitrate). We are interested in multicast distribution of image content: all nodes decode and display the image, but at possibly different qualities. Each node is also permitted to perform transcoding before sending data to another node. This simple network flow graph applies equally to live streaming and file sharing, since it does not consider intermittent effects such as congestion, packet losses or departure of peers. Given such a network flow graph, we are interested in the fundamental question: how can one achieve the best image quality across the network? The general answer is far from clear, even for very small networks. Information theoretic bounds are available for a few special cases that transform into other problems, such as successive refinement [1] and multiple description coding [2], [3]. We focus on practical image multicast in the two-hop mesh network shown in Fig. 1, for which tight theoretical bounds have yet to be derived. In this network, a source node S communicates image content to viewers at intermediate nodes N i (for 1 i k) and a terminal node T. The first-hop links SN i support the same rate equal to R bits per pixel (bpp) of the original image. The second-hop links N i T support different rates equal to R i bpp of the original image, where R i R. For the case R i = R, Sarshar and Wu showed that multiple description coding produces a better quality reconstruction at node T than single description coding, with little degradation of quality at the intermediate nodes N i [4]. Each intermediate node receives a different description of the original image and relays it to the terminal node, which combines them into a higher quality reconstruction. But they did not consider the possibility of transcoding at intermediate nodes, if the second-hop links support lower bitrate than the first-hop links. In this paper, we demonstrate that Wyner- Ziv transcoding [5] of the multiple descriptions exploits the redundancy between descriptions, improving network image coding performance beyond conventional transcoding. In Section II, we consider the special case of k = 2, for which the network has diamond topology, and argue that any network which contains a diamond subnet with certain link capacities will benefit from Wyner-Ziv transcoding. Section III describes the implementation of the single description, multiple description and Wyner-Ziv image codecs. Their performance in image multicast, for k = 2 and k = 4, is reported in Section IV. II. DIAMOND NETWORK The network with k = 2 is the simplest nontrivial form of the two-hop mesh network in Fig. 1. For simplicity, we fix the rate R 1 = R, but allow R 2 to vary from 0 to R. Case 1: R 2 = 0. The network reduces to a tree, and each node has total downlink rate R. The source node S should encode the image to a single description at rate R and distribute this encoding along links SN 1, SN 2 and N 1 T. Case 2: R 2 = R. In this symmetric case, the terminal node T receives total network flow of 2R. If, as in Case 1, nodes N 1 and N 2 receive the same description encoded at rate R, then T is limited to that quality. Sarshar and Wu [4] showed that it is better to send different rate-r descriptions to N 1 and

2 i th Description (rate R) Reconstructed i th Description 8x8 DCT Wyner-Ziv Encoder at Node N i Inverse 8x8 DCT Quantizer Inverse Quantizer Gray Mapper LDPC Encoder LDPC/Gray Decoder Syndrome (rate R i ) Wyner-Ziv Decoder at Node T 8x8 DCT Fig. 2. Polyphase subsampling of image pixels to create four descriptions. Interpolated 1 st to (i-1) th Descriptions N 2, so that T can combine the multiple descriptions into a higher quality reconstruction than in Case 1. Case 3: 0 < R 2 < R. When link N 2 T supports a lower rate than the other links, node T receives network flow of R + R 2. The multiple description approach of Case 2 can be modified as follows. The first description (of rate R) is relayed to node T via node N 1. The second description is transcoded at node N 2 from rate R to R 2. Since the first description is correlated with the second and is already available at node T, the transcoding can exploit the redundancy between the descriptions. For this reason, Wyner-Ziv transcoding of the second description at node N 2 offers a higher quality second description than conventional transcoding. This in turn improves the quality of the overall reconstruction at node T. In larger mesh networks containing one or more diamond subnets with link capacities as in Case 3, the improvement is greater because more links can benefit from Wyner-Ziv transcoding and the better reconstruction quality may propagate to downstream viewers. In particular, the two-hop mesh network in Fig. 1 consists of k 1 nested diamond subnets. At the top level, one can group the links into two bundles passing through complementary subsets of the intermediate nodes. III. NETWORK IMAGE CODECS A. Single Description Coding We use the JPEG standard to generate a single description of the image. To avoid blocking artifacts at low rate R, the image is subsampled before applying JPEG. We minimize aliasing in resampling using a Lanczos-3 interpolation kernel [6]. B. Multiple Description Coding We create spatially-interleaved multiple descriptions of an image via polyphase subsampling [7]. As shown in Fig. 2, the pixels are divided into four phases, each of which is encoded with JPEG into one of four descriptions. To preserve the original resolution, we do not prefilter the image before polyphase subsampling. Instead, we apply different smoothing kernels at reconstruction depending on which descriptions are available at each node. The intermediate nodes receive one of the multiple descriptions at quarter of the original resolution. We postfilter the JPEG reconstruction to reduce aliasing artifacts. The terminal node in the k = 2 network receives the first and second Fig. 3. Wyner-Ziv codec: encoder at node N i and a decoder at node T. descriptions only, possibly transcoded. These two descriptions are smoothed and missing pixels from the third and fourth phases are interpolated bilinearly. In the k = 4 network, the terminal node receives all four descriptions, possibly transcoded, to which we apply a smoothing postfilter. C. Wyner-Ziv Transcoding of Multiple Descriptions In the k = 2 diamond network described in Section II, the Wyner-Ziv transcoder employs an encoder at node N 2 and a decoder at node T. In line with the principles of source coding with decoder side information, the encoder transcodes the second description in the absence of the first description, and the decoder reconstructs the second description with reference to the first. The decoder side information is the bilinear interpolation of the first description to the pixel positions of the second phase. In the network with k = 4, the second description is transcoded as above. The third and fourth descriptions are also Wyner-Ziv transcoded for transmission along links N 3 T and N 4 T. The decoder side information is the bilinear interpolation of the first two descriptions to the positions of the third and fourth phases, respectively. Fig. 3 shows the block diagram of the Wyner-Ziv codec for transcoding the i th description at intermediate node N i, and reconstructing it in the presence of the first to (i 1) th descriptions at terminal node T. At the Wyner-Ziv encoder, the i th description is transformed blockwise using an 8 8 DCT in order to concentrate the signal energy to a few transform coefficients. Then the coefficients are quantized with a quantization matrix specified by a single quality factor. The quantization indices are binarized using a Gray mapping that maximizes bitwise correlation with the binarized version of the side information. Finally, the Gray bits are encoded into the syndrome of a low-density parity-check (LDPC) code, as in [8], to a rate R i bpp of the original image. The choice of quantization matrix quality factor is critical to whether the transmitted LDPC syndrome is decodable at terminal node T, because the bitstream from a coarse quantizer can be decoded (with reference to the side information) at lower rates than the bitstream from a finer quantizer. The

3 Syndrome Nodes Gray Bit Nodes Coefficient Nodes Fig. 4. Example LDPC/Gray decoder factor graph. transcoded) multiple descriptions are postfiltered with 3 3 kernels that mitigate visual artifacts while blurring the image as little as possible. A. Single Description For both the k = 2 and k = 4 networks, we set R = 0.23 bpp of the original image and R 1 = R. At this setting, a single description can be coded directly at quality factor 10, but it is subjectively better to subsample the image to quarter resolution with a Lanczos-3 kernel and code it at quality factor 54. Under single description coding, the same reconstruction at quarter resolution, shown in Fig. 5, is viewed at all nodes N i and T. decision is made in advance at the source node S, since it has access to all descriptions, and the selected quality factor is communicated to the Wyner-Ziv encoder at node N i at negligible additional rate. The Wyner-Ziv decoder at node T operates as follows. It receives the LDPC syndrome at an LDPC/Gray decoder, where all Gray bitplanes of the transcoded quantization indices of the i th description are recovered jointly, using the DCT coefficients of the side information. Following inverse quantization and inverse 8 8 DCT, the i th description is reconstructed. The LDPC/Gray decoder has a factor graph like the one in Fig. 4 [9]. It is an LDPC factor graph augmented with nodes representing each coefficient to be decoded. The DCT coefficients of the side information seed the coefficient nodes with probability distributions. These beliefs are propagated through the graph, reconciled with the received values in the syndrome nodes, and propagated back to the coefficient nodes, by means of the sum-product algorithm [9]. As mentioned above, the appropriate selection of quantization matrix quality factor guarantees that the coefficient distributions will converge after a fixed number of sum-product iterations. Further explanation of the LDPC/Gray decoder is available in [10] and [11]. IV. SIMULATION RESULTS In the following simulations, we multicast the luminance channel of the Lena image of original resolution over the k = 2 and k = 4 networks, using single description coding and multiple description coding with and without Wyner-Ziv transcoding. The coding and transcoding steps employ commonly-used quantization matrices, specified by the JPEG quality factor ranging from 0 to 100. A higher value represents higher quality and the value 50 gives the matrix in Annex K of the JPEG standard [12]. With respect to an original quality factor of 50, we find that transcoding (whether conventional or Wyner-Ziv) at quality factors 6, 8, 12, 23 or 50 is rate-distortion efficient. The Wyner-Ziv transcoder treats a description as 16 separate tiles of resolution It represents quantization indices to 8 Gray bits. We implement the LDPC code as a regular degree 3 code of length bits and flexible rate [13]. The reconstructions from (possibly B. Multiple Descriptions at Intermediate Nodes When R = 0.23 bpp of the original image for k = 2 or k = 4, the multiple descriptions obtained from polyphase subsampling can be coded at quality factor 50. Since the multiple descriptions are not antialias prefiltered, each description is postfiltered for viewing at its respective intermediate node, using the filter kernel This kernel produces the reconstruction of the first description shown in Fig. 6, which is representative of the reconstructions at other intermediate nodes. This reconstruction is slightly blurrier than the single description reconstruction, but superior full resolution reconstructions at the terminal node now become possible. C. Multiple Descriptions at Terminal Node for k = 2 In the k = 2 network, the terminal node T receives the first and second descriptions of the image. Since R 1 = R, node N 1 simply relays the unfiltered first description to T without transcoding. Fig. 7 plots the rate R 2 (normalized by R) required for conventional and Wyner-Ziv transcoding the unfiltered second description at node N 2 at different quality factors. The rate required increases with greater quality for both transcoding methods, but Wyner-Ziv transcoding requires significantly less rate than conventional transcoding at equal quality, because it exploits the redundancy between the descriptions. Observe that around link capacity of R 2 = 0.32R, Wyner-Ziv transcoding operates at quality factor 23, but conventional transcoding operates at only 8. The terminal node T combines the first and transcoded second descriptions in two postfiltering steps. The pixels of the two received descriptions are smoothed using the filter kernel Note that the zeros lie on the unoccupied pixel positions of the third and fourth phases of the original image. Next these third and fourth phase positions are bilinearly interpolated.

4 1 0.8 Conventional transcoding Wyner Ziv transcoding 0.6 R 2 /R Fig. 5. Single description reconstruction (quarter resolution) at all nodes N i and T, for R = 0.23 bpp of original image and R 1 = R Transcoding quality factor at N 2 Fig. 7. Required rate R 2 for conventional and Wyner-Ziv transcoding at node N 2 using different quality factors. Fig. 6. Multiple description reconstruction (quarter resolution) at node N 1 (representative of other nodes N i ), for R = 0.23 bpp of original image. With R 2 = 0.32R, Figs. 8(a) and (b) show the reconstructions at terminal node T using conventional transcoding at quality factor 8 and Wyner-Ziv transcoding at quality factor 23, respectively. Both of these reconstructions are at full resolution, higher than the single description reconstruction in Fig. 5, but only the reconstruction resulting from Wyner-Ziv transcoding is visually superior. The reconstruction resulting from conventional transcoding suffers from blocking artifacts that cannot be eliminated by filtering with a 3 3 kernel and bilinear interpolation. D. Multiple Descriptions at Terminal Node for k = 4 The k = 4 network contains the k = 2 network as a subnet. In this section, we fix the following link rates: R 1 = R and R 2 = 0.32R. Thus, the terminal node T obtains the first and second descriptions just as in the k = 2 network described in the previous section. Specifically, the first description is received coded at quality factor 50 and the second description at quality factor 8 if conventionally transcoded, or at quality factor 23 if Wyner-Ziv transcoded. Node T additionally receives the third and fourth descriptions via nodes N 3 and N 4. Figs. 9(a) and (b) plot the rates R 3 and R 4 required to transcode the unfiltered third and fourth descriptions, respectively, at different quality factors. Just like transcoding the second description, the Wyner-Ziv transcoder requires much less rate than conventional transcoding at the same quality. Notice, in particular, that around link capacities of R 3 = R 4 = 0.28R, Wyner-Ziv transcoding operates at quality factor 23, but conventional transcoding operates at only 6, for both the third and fourth descriptions. At reconstruction, the pixels of all four received descriptions are postfiltered using the filter kernel All 8 neighbors of a pixel are weighted equally in this filter because quantization mismatch between descriptions (rather than aliasing) creates the most visible artifacts. For the setting R 3 = R 4 = 0.28R, Figs. 10(a) and (b) show the reconstructions at terminal node T with the third and fourth descriptions conventional transcoded at quality factor 6 and Wyner-Ziv transcoded at quality factor 23, respectively. As in the k = 2 network, the reconstruction resulting from Wyner- Ziv transcoding is visually superior to the one resulting from conventional transcoding, which suffers from severe blocking artifacts. Moreover, the k = 4 reconstruction in Fig. 10(b) is slightly sharper than the k = 2 reconstruction in Fig. 8(b), especially around horizontal and vertical edges like those found in Lena s hair.

5 (a) (b) Fig. 8. Multiple description reconstructions (full resolution) in k = 2 network at terminal node T, for R = 0.23 bpp of original image, R 1 = R and R 2 = 0.32R, using (a) conventional transcoding and (b) Wyner-Ziv transcoding. V. CONCLUSIONS We have investigated the problem of network image coding for multicast. All nodes in the multicast network decode and display reconstructions of the image (at possibly different qualities), and may also transcode their reconstructions for transmission to nodes downstream in the network. We showed that multiple description coding combined with Wyner-Ziv transcoding offers better reconstruction quality than multiple description coding combined with conventional transcoding, for the diamond network over a wide range of link rate settings. Furthermore, we argued that this property extends to more complicated networks that contain one or more diamond subnets. Larger networks benefit from more links that can use Wyner-Ziv transcoding and better reconstruction quality cascading to a larger number of viewers downstream. We performed experiments with four spatially-interleaved multiple descriptions, using two in the diamond network and all four in a two-hop mesh network with four branches. Our results showed that Wyner-Ziv transcoding can produce image reconstructions of superior visual quality compared to single description coding and conventional transcoding of multiple descriptions. In future work, we will apply these techniques to network video coding using temporally-interleaved multiple descriptions. Ultimately, we are interested in the theory and practice of network video coding for multicast over general network topologies. ACKNOWLEDGMENT This work has been supported, in part, by a gift from HP Labs. REFERENCES [1] W. Equitz and T. Cover, Successive refinement of information, IEEE Trans. Inform. Theory, vol. 37, no. 2, pp , Mar [2] L. Ozarow, On a source-coding problem with two channels and three receivers, Bell Sys. Tech. J, vol. 59, no. 10, pp , Dec [3] A. A. El Gamal and T. Cover, Achievable rates for multiple descriptions, IEEE Trans. Inform. Theory, vol. 28, no. 6, pp , Nov [4] N. Sarshar and X. Wu, Rate-distortion optimized multimedia communication in networks, in Proc. Visual Commun. and Image Processing, San Jose, CA, [5] A. D. Wyner and J. Ziv, The rate-distortion function for source coding with side information at the decoder, IEEE Trans. Inform. Theory, vol. 22, no. 1, pp. 1 10, Jan [6] C. E. Duchon, Lanczos filtering in one and two dimensions, J. Appl. Meteor., vol. 18, pp , [7] P. Subrahmanya and T. Berger, Multiple descriptions encoding of images, in Proc. IEEE Data Compression Conf., Snowbird, UT, [8] A. Liveris, Z. Xiong, and C. Georghiades, Compression of binary sources with side information at the decoder using LDPC codes, IEEE Commun. Lett., vol. 6, no. 10, pp , Oct [9] F. R. Kschischang, B. J. Frey, and H.-A. Loeliger, Factor graphs and the sum-product algorithm, IEEE Trans. Inform. Theory, vol. 47, no. 2, pp , Feb [10] D. Varodayan, D. Chen, M. Flierl, and B. Girod, Wyner-Ziv coding of video with unsupervised motion vector learning, EURASIP Signal Processing: Image Commun. J., vol. 23, no. 5, pp , [11] D. Chen, D. Varodayan, M. Flierl, and B. Girod, Wyner-Ziv coding of multiview images with unsupervised learning of disparity and Gray code, in Proc. IEEE Internat. Conf. Image Processing, San Diego, CA, [12] ITU-T and I. JTC1, Digital compression and coding of continuous-tone still images, ISO/IEC ITU-T Recommendation T.81 (JPEG), Sept [13] D. Varodayan, A. Aaron, and B. Girod, Rate-adaptive codes for distributed source coding, EURASIP Signal Processing J., vol. 86, no. 11, pp , Nov

6 1 0.8 Conventional transcoding Wyner Ziv transcoding Conventional transcoding Wyner Ziv transcoding R 3 /R 0.4 R 4 /R Transcoding quality factor at N 3 Transcoding quality factor at N 4 (a) (b) Fig. 9. Required rates for conventional and Wyner-Ziv transcoding, (a) R 3 at node N 3 and (b) R 4 at node N 4, using different quality factors. (a) (b) Fig. 10. Multiple description reconstructions (full resolution) in k = 4 network at terminal node T, for R = 0.23 bpp of original image, R 1 = R, R 2 = 0.32R and R 3 = R 4 = 0.28R, using (a) conventional transcoding and (b) Wyner-Ziv transcoding.

Distributed Grayscale Stereo Image Coding with Improved Disparity and Noise Estimation

Distributed Grayscale Stereo Image Coding with Improved Disparity and Noise Estimation Distributed Grayscale Stereo Image Coding with Improved Disparity and Noise Estimation David Chen Dept. Electrical Engineering Stanford University Email: dmchen@stanford.edu Abstract The problem of distributed

More information

Frequency Band Coding Mode Selection for Key Frames of Wyner-Ziv Video Coding

Frequency Band Coding Mode Selection for Key Frames of Wyner-Ziv Video Coding 2009 11th IEEE International Symposium on Multimedia Frequency Band Coding Mode Selection for Key Frames of Wyner-Ziv Video Coding Ghazaleh R. Esmaili and Pamela C. Cosman Department of Electrical and

More information

Distributed Video Coding

Distributed Video Coding Distributed Video Coding Bernd Girod Anne Aaron Shantanu Rane David Rebollo-Monedero David Varodayan Information Systems Laboratory Stanford University Outline Lossless and lossy compression with receiver

More information

Distributed Grayscale Stereo Image Coding with Unsupervised Learning of Disparity

Distributed Grayscale Stereo Image Coding with Unsupervised Learning of Disparity Distributed Grayscale Stereo Image Coding with Unsupervised Learning of Disparity David Varodayan, Aditya Mavlankar, Markus Flierl and Bernd Girod Max Planck Center for Visual Computing and Communication

More information

Compression of Stereo Images using a Huffman-Zip Scheme

Compression of Stereo Images using a Huffman-Zip Scheme Compression of Stereo Images using a Huffman-Zip Scheme John Hamann, Vickey Yeh Department of Electrical Engineering, Stanford University Stanford, CA 94304 jhamann@stanford.edu, vickey@stanford.edu Abstract

More information

Research on Distributed Video Compression Coding Algorithm for Wireless Sensor Networks

Research on Distributed Video Compression Coding Algorithm for Wireless Sensor Networks Sensors & Transducers 203 by IFSA http://www.sensorsportal.com Research on Distributed Video Compression Coding Algorithm for Wireless Sensor Networks, 2 HU Linna, 2 CAO Ning, 3 SUN Yu Department of Dianguang,

More information

QUANTIZER DESIGN FOR EXPLOITING COMMON INFORMATION IN LAYERED CODING. Mehdi Salehifar, Tejaswi Nanjundaswamy, and Kenneth Rose

QUANTIZER DESIGN FOR EXPLOITING COMMON INFORMATION IN LAYERED CODING. Mehdi Salehifar, Tejaswi Nanjundaswamy, and Kenneth Rose QUANTIZER DESIGN FOR EXPLOITING COMMON INFORMATION IN LAYERED CODING Mehdi Salehifar, Tejaswi Nanjundaswamy, and Kenneth Rose Department of Electrical and Computer Engineering University of California,

More information

CONTENT ADAPTIVE SCREEN IMAGE SCALING

CONTENT ADAPTIVE SCREEN IMAGE SCALING CONTENT ADAPTIVE SCREEN IMAGE SCALING Yao Zhai (*), Qifei Wang, Yan Lu, Shipeng Li University of Science and Technology of China, Hefei, Anhui, 37, China Microsoft Research, Beijing, 8, China ABSTRACT

More information

A 3-D Virtual SPIHT for Scalable Very Low Bit-Rate Embedded Video Compression

A 3-D Virtual SPIHT for Scalable Very Low Bit-Rate Embedded Video Compression A 3-D Virtual SPIHT for Scalable Very Low Bit-Rate Embedded Video Compression Habibollah Danyali and Alfred Mertins University of Wollongong School of Electrical, Computer and Telecommunications Engineering

More information

Optimal Estimation for Error Concealment in Scalable Video Coding

Optimal Estimation for Error Concealment in Scalable Video Coding Optimal Estimation for Error Concealment in Scalable Video Coding Rui Zhang, Shankar L. Regunathan and Kenneth Rose Department of Electrical and Computer Engineering University of California Santa Barbara,

More information

SIGNAL COMPRESSION. 9. Lossy image compression: SPIHT and S+P

SIGNAL COMPRESSION. 9. Lossy image compression: SPIHT and S+P SIGNAL COMPRESSION 9. Lossy image compression: SPIHT and S+P 9.1 SPIHT embedded coder 9.2 The reversible multiresolution transform S+P 9.3 Error resilience in embedded coding 178 9.1 Embedded Tree-Based

More information

Rate-distortion Optimized Streaming of Compressed Light Fields with Multiple Representations

Rate-distortion Optimized Streaming of Compressed Light Fields with Multiple Representations Rate-distortion Optimized Streaming of Compressed Light Fields with Multiple Representations Prashant Ramanathan and Bernd Girod Department of Electrical Engineering Stanford University Stanford CA 945

More information

Mesh Based Interpolative Coding (MBIC)

Mesh Based Interpolative Coding (MBIC) Mesh Based Interpolative Coding (MBIC) Eckhart Baum, Joachim Speidel Institut für Nachrichtenübertragung, University of Stuttgart An alternative method to H.6 encoding of moving images at bit rates below

More information

Optimized Progressive Coding of Stereo Images Using Discrete Wavelet Transform

Optimized Progressive Coding of Stereo Images Using Discrete Wavelet Transform Optimized Progressive Coding of Stereo Images Using Discrete Wavelet Transform Torsten Palfner, Alexander Mali and Erika Müller Institute of Telecommunications and Information Technology, University of

More information

A LOW-COMPLEXITY MULTIPLE DESCRIPTION VIDEO CODER BASED ON 3D-TRANSFORMS

A LOW-COMPLEXITY MULTIPLE DESCRIPTION VIDEO CODER BASED ON 3D-TRANSFORMS A LOW-COMPLEXITY MULTIPLE DESCRIPTION VIDEO CODER BASED ON 3D-TRANSFORMS Andrey Norkin, Atanas Gotchev, Karen Egiazarian, Jaakko Astola Institute of Signal Processing, Tampere University of Technology

More information

Modified SPIHT Image Coder For Wireless Communication

Modified SPIHT Image Coder For Wireless Communication Modified SPIHT Image Coder For Wireless Communication M. B. I. REAZ, M. AKTER, F. MOHD-YASIN Faculty of Engineering Multimedia University 63100 Cyberjaya, Selangor Malaysia Abstract: - The Set Partitioning

More information

Compression of VQM Features for Low Bit-Rate Video Quality Monitoring

Compression of VQM Features for Low Bit-Rate Video Quality Monitoring Compression of VQM Features for Low Bit-Rate Video Quality Monitoring Mina Makar, Yao-Chung Lin, Andre F. de Araujo and Bernd Girod Information Systems Laboratory, Stanford University, Stanford, CA 9435

More information

LOSSLESS COMPRESSION OF ENCRYPTED GREY-LEVEL AND COLOR IMAGES

LOSSLESS COMPRESSION OF ENCRYPTED GREY-LEVEL AND COLOR IMAGES LOSSLESS COMPRESSION OF ENCRYPTED GREY-LEVEL AND COLOR IMAGES Riccardo Lazzeretti, Mauro Barni Department of Information Engineering (University of Siena) Via Roma 56, 53100, Siena, Italy email: lazzeretti2@unisi.it

More information

Complexity Efficient Stopping Criterion for LDPC Based Distributed Video Coding

Complexity Efficient Stopping Criterion for LDPC Based Distributed Video Coding Complexity Efficient Stopping Criterion for LDPC Based Distributed Video Coding João Ascenso Instituto Superior de Engenharia de Lisboa Instituto de Telecomunicações Rua Conselheiro Emídio Navarro, 1 195-62

More information

Video Transcoding Architectures and Techniques: An Overview. IEEE Signal Processing Magazine March 2003 Present by Chen-hsiu Huang

Video Transcoding Architectures and Techniques: An Overview. IEEE Signal Processing Magazine March 2003 Present by Chen-hsiu Huang Video Transcoding Architectures and Techniques: An Overview IEEE Signal Processing Magazine March 2003 Present by Chen-hsiu Huang Outline Background & Introduction Bit-rate Reduction Spatial Resolution

More information

Homogeneous Transcoding of HEVC for bit rate reduction

Homogeneous Transcoding of HEVC for bit rate reduction Homogeneous of HEVC for bit rate reduction Ninad Gorey Dept. of Electrical Engineering University of Texas at Arlington Arlington 7619, United States ninad.gorey@mavs.uta.edu Dr. K. R. Rao Fellow, IEEE

More information

Review and Implementation of DWT based Scalable Video Coding with Scalable Motion Coding.

Review and Implementation of DWT based Scalable Video Coding with Scalable Motion Coding. Project Title: Review and Implementation of DWT based Scalable Video Coding with Scalable Motion Coding. Midterm Report CS 584 Multimedia Communications Submitted by: Syed Jawwad Bukhari 2004-03-0028 About

More information

Study and Development of Image Authentication using Slepian Wolf Coding

Study and Development of Image Authentication using Slepian Wolf Coding e t International Journal on Emerging Technologies 6(2): 126-130(2015) ISSN No. (Print) : 0975-8364 ISSN No. (Online) : 2249-3255 Study and Development of Image Authentication using Slepian Wolf Coding

More information

Image Segmentation Techniques for Object-Based Coding

Image Segmentation Techniques for Object-Based Coding Image Techniques for Object-Based Coding Junaid Ahmed, Joseph Bosworth, and Scott T. Acton The Oklahoma Imaging Laboratory School of Electrical and Computer Engineering Oklahoma State University {ajunaid,bosworj,sacton}@okstate.edu

More information

ENCODER POWER CONSUMPTION COMPARISON OF DISTRIBUTED VIDEO CODEC AND H.264/AVC IN LOW-COMPLEXITY MODE

ENCODER POWER CONSUMPTION COMPARISON OF DISTRIBUTED VIDEO CODEC AND H.264/AVC IN LOW-COMPLEXITY MODE ENCODER POWER CONSUMPTION COMPARISON OF DISTRIBUTED VIDEO CODEC AND H.64/AVC IN LOW-COMPLEXITY MODE Anna Ukhanova, Eugeniy Belyaev and Søren Forchhammer Technical University of Denmark, DTU Fotonik, B.

More information

FAST AND EFFICIENT SPATIAL SCALABLE IMAGE COMPRESSION USING WAVELET LOWER TREES

FAST AND EFFICIENT SPATIAL SCALABLE IMAGE COMPRESSION USING WAVELET LOWER TREES FAST AND EFFICIENT SPATIAL SCALABLE IMAGE COMPRESSION USING WAVELET LOWER TREES J. Oliver, Student Member, IEEE, M. P. Malumbres, Member, IEEE Department of Computer Engineering (DISCA) Technical University

More information

Multi-View Image Coding in 3-D Space Based on 3-D Reconstruction

Multi-View Image Coding in 3-D Space Based on 3-D Reconstruction Multi-View Image Coding in 3-D Space Based on 3-D Reconstruction Yongying Gao and Hayder Radha Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48823 email:

More information

Compression Algorithms for Flexible Video Decoding

Compression Algorithms for Flexible Video Decoding Compression Algorithms for Flexible Video Decoding Ngai-Man Cheung and Antonio Ortega Signal and Image Processing Institute, Univ. of Southern California, Los Angeles, CA ABSTRACT We investigate compression

More information

Lossless and Lossy Minimal Redundancy Pyramidal Decomposition for Scalable Image Compression Technique

Lossless and Lossy Minimal Redundancy Pyramidal Decomposition for Scalable Image Compression Technique Lossless and Lossy Minimal Redundancy Pyramidal Decomposition for Scalable Image Compression Technique Marie Babel, Olivier Déforges To cite this version: Marie Babel, Olivier Déforges. Lossless and Lossy

More information

Depth Estimation for View Synthesis in Multiview Video Coding

Depth Estimation for View Synthesis in Multiview Video Coding MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Depth Estimation for View Synthesis in Multiview Video Coding Serdar Ince, Emin Martinian, Sehoon Yea, Anthony Vetro TR2007-025 June 2007 Abstract

More information

H.264 STANDARD BASED SIDE INFORMATION GENERATION IN WYNER-ZIV CODING

H.264 STANDARD BASED SIDE INFORMATION GENERATION IN WYNER-ZIV CODING H.264 STANDARD BASED SIDE INFORMATION GENERATION IN WYNER-ZIV CODING SUBRAHMANYA MAIRA VENKATRAV Supervising Professor: Dr. K. R. Rao 1 TABLE OF CONTENTS 1. Introduction 1.1. Wyner-Ziv video coding 1.2.

More information

signal-to-noise ratio (PSNR), 2

signal-to-noise ratio (PSNR), 2 u m " The Integration in Optics, Mechanics, and Electronics of Digital Versatile Disc Systems (1/3) ---(IV) Digital Video and Audio Signal Processing ƒf NSC87-2218-E-009-036 86 8 1 --- 87 7 31 p m o This

More information

ESTIMATION OF THE UTILITIES OF THE NAL UNITS IN H.264/AVC SCALABLE VIDEO BITSTREAMS. Bin Zhang, Mathias Wien and Jens-Rainer Ohm

ESTIMATION OF THE UTILITIES OF THE NAL UNITS IN H.264/AVC SCALABLE VIDEO BITSTREAMS. Bin Zhang, Mathias Wien and Jens-Rainer Ohm 19th European Signal Processing Conference (EUSIPCO 2011) Barcelona, Spain, August 29 - September 2, 2011 ESTIMATION OF THE UTILITIES OF THE NAL UNITS IN H.264/AVC SCALABLE VIDEO BITSTREAMS Bin Zhang,

More information

Rate-distortion Optimized Streaming of Compressed Light Fields with Multiple Representations

Rate-distortion Optimized Streaming of Compressed Light Fields with Multiple Representations Rate-distortion Optimized Streaming of Compressed Light Fields with Multiple Representations Prashant Ramanathan and Bernd Girod Department of Electrical Engineering Stanford University Stanford CA 945

More information

Optimizing the Deblocking Algorithm for. H.264 Decoder Implementation

Optimizing the Deblocking Algorithm for. H.264 Decoder Implementation Optimizing the Deblocking Algorithm for H.264 Decoder Implementation Ken Kin-Hung Lam Abstract In the emerging H.264 video coding standard, a deblocking/loop filter is required for improving the visual

More information

Adaptive Quantization for Video Compression in Frequency Domain

Adaptive Quantization for Video Compression in Frequency Domain Adaptive Quantization for Video Compression in Frequency Domain *Aree A. Mohammed and **Alan A. Abdulla * Computer Science Department ** Mathematic Department University of Sulaimani P.O.Box: 334 Sulaimani

More information

EXPLORING ON STEGANOGRAPHY FOR LOW BIT RATE WAVELET BASED CODER IN IMAGE RETRIEVAL SYSTEM

EXPLORING ON STEGANOGRAPHY FOR LOW BIT RATE WAVELET BASED CODER IN IMAGE RETRIEVAL SYSTEM TENCON 2000 explore2 Page:1/6 11/08/00 EXPLORING ON STEGANOGRAPHY FOR LOW BIT RATE WAVELET BASED CODER IN IMAGE RETRIEVAL SYSTEM S. Areepongsa, N. Kaewkamnerd, Y. F. Syed, and K. R. Rao The University

More information

Multiframe Blocking-Artifact Reduction for Transform-Coded Video

Multiframe Blocking-Artifact Reduction for Transform-Coded Video 276 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 12, NO. 4, APRIL 2002 Multiframe Blocking-Artifact Reduction for Transform-Coded Video Bahadir K. Gunturk, Yucel Altunbasak, and

More information

Vidhya.N.S. Murthy Student I.D Project report for Multimedia Processing course (EE5359) under Dr. K.R. Rao

Vidhya.N.S. Murthy Student I.D Project report for Multimedia Processing course (EE5359) under Dr. K.R. Rao STUDY AND IMPLEMENTATION OF THE MATCHING PURSUIT ALGORITHM AND QUALITY COMPARISON WITH DISCRETE COSINE TRANSFORM IN AN MPEG2 ENCODER OPERATING AT LOW BITRATES Vidhya.N.S. Murthy Student I.D. 1000602564

More information

Week 14. Video Compression. Ref: Fundamentals of Multimedia

Week 14. Video Compression. Ref: Fundamentals of Multimedia Week 14 Video Compression Ref: Fundamentals of Multimedia Last lecture review Prediction from the previous frame is called forward prediction Prediction from the next frame is called forward prediction

More information

Reconstruction PSNR [db]

Reconstruction PSNR [db] Proc. Vision, Modeling, and Visualization VMV-2000 Saarbrücken, Germany, pp. 199-203, November 2000 Progressive Compression and Rendering of Light Fields Marcus Magnor, Andreas Endmann Telecommunications

More information

Video Compression An Introduction

Video Compression An Introduction Video Compression An Introduction The increasing demand to incorporate video data into telecommunications services, the corporate environment, the entertainment industry, and even at home has made digital

More information

Region-based Fusion Strategy for Side Information Generation in DMVC

Region-based Fusion Strategy for Side Information Generation in DMVC Region-based Fusion Strategy for Side Information Generation in DMVC Yongpeng Li * a, Xiangyang Ji b, Debin Zhao c, Wen Gao d a Graduate University, Chinese Academy of Science, Beijing, 39, China; b Institute

More information

Error Protection of Wavelet Coded Images Using Residual Source Redundancy

Error Protection of Wavelet Coded Images Using Residual Source Redundancy Error Protection of Wavelet Coded Images Using Residual Source Redundancy P. Greg Sherwood and Kenneth Zeger University of California San Diego 95 Gilman Dr MC 47 La Jolla, CA 9293 sherwood,zeger @code.ucsd.edu

More information

DIGITAL TELEVISION 1. DIGITAL VIDEO FUNDAMENTALS

DIGITAL TELEVISION 1. DIGITAL VIDEO FUNDAMENTALS DIGITAL TELEVISION 1. DIGITAL VIDEO FUNDAMENTALS Television services in Europe currently broadcast video at a frame rate of 25 Hz. Each frame consists of two interlaced fields, giving a field rate of 50

More information

JPEG Joint Photographic Experts Group ISO/IEC JTC1/SC29/WG1 Still image compression standard Features

JPEG Joint Photographic Experts Group ISO/IEC JTC1/SC29/WG1 Still image compression standard Features JPEG-2000 Joint Photographic Experts Group ISO/IEC JTC1/SC29/WG1 Still image compression standard Features Improved compression efficiency (vs. JPEG) Highly scalable embedded data streams Progressive lossy

More information

PERFORMANCE ANALYSIS OF INTEGER DCT OF DIFFERENT BLOCK SIZES USED IN H.264, AVS CHINA AND WMV9.

PERFORMANCE ANALYSIS OF INTEGER DCT OF DIFFERENT BLOCK SIZES USED IN H.264, AVS CHINA AND WMV9. EE 5359: MULTIMEDIA PROCESSING PROJECT PERFORMANCE ANALYSIS OF INTEGER DCT OF DIFFERENT BLOCK SIZES USED IN H.264, AVS CHINA AND WMV9. Guided by Dr. K.R. Rao Presented by: Suvinda Mudigere Srikantaiah

More information

Coding of Coefficients of two-dimensional non-separable Adaptive Wiener Interpolation Filter

Coding of Coefficients of two-dimensional non-separable Adaptive Wiener Interpolation Filter Coding of Coefficients of two-dimensional non-separable Adaptive Wiener Interpolation Filter Y. Vatis, B. Edler, I. Wassermann, D. T. Nguyen and J. Ostermann ABSTRACT Standard video compression techniques

More information

Video Compression Method for On-Board Systems of Construction Robots

Video Compression Method for On-Board Systems of Construction Robots Video Compression Method for On-Board Systems of Construction Robots Andrei Petukhov, Michael Rachkov Moscow State Industrial University Department of Automatics, Informatics and Control Systems ul. Avtozavodskaya,

More information

The Scope of Picture and Video Coding Standardization

The Scope of Picture and Video Coding Standardization H.120 H.261 Video Coding Standards MPEG-1 and MPEG-2/H.262 H.263 MPEG-4 H.264 / MPEG-4 AVC Thomas Wiegand: Digital Image Communication Video Coding Standards 1 The Scope of Picture and Video Coding Standardization

More information

Fast Progressive Image Coding without Wavelets

Fast Progressive Image Coding without Wavelets IEEE DATA COMPRESSION CONFERENCE SNOWBIRD, UTAH, MARCH 2000 Fast Progressive Image Coding without Wavelets Henrique S. Malvar Microsoft Research One Microsoft Way, Redmond, WA 98052 malvar@microsoft.com

More information

Advanced Video Coding: The new H.264 video compression standard

Advanced Video Coding: The new H.264 video compression standard Advanced Video Coding: The new H.264 video compression standard August 2003 1. Introduction Video compression ( video coding ), the process of compressing moving images to save storage space and transmission

More information

Multiple Constraint Satisfaction by Belief Propagation: An Example Using Sudoku

Multiple Constraint Satisfaction by Belief Propagation: An Example Using Sudoku Multiple Constraint Satisfaction by Belief Propagation: An Example Using Sudoku Todd K. Moon and Jacob H. Gunther Utah State University Abstract The popular Sudoku puzzle bears structural resemblance to

More information

Pre- and Post-Processing for Video Compression

Pre- and Post-Processing for Video Compression Whitepaper submitted to Mozilla Research Pre- and Post-Processing for Video Compression Aggelos K. Katsaggelos AT&T Professor Department of Electrical Engineering and Computer Science Northwestern University

More information

WZS: WYNER-ZIV SCALABLE PREDICTIVE VIDEO CODING. Huisheng Wang, Ngai-Man Cheung and Antonio Ortega

WZS: WYNER-ZIV SCALABLE PREDICTIVE VIDEO CODING. Huisheng Wang, Ngai-Man Cheung and Antonio Ortega WZS: WYNERZIV SCALABLE PREDICTIVE VIDEO CODING Huisheng Wang, NgaiMan Cheung and Antonio Ortega Integrated Media Systems Center and Department of Electrical Engineering University of Southern California,

More information

Intra-Mode Indexed Nonuniform Quantization Parameter Matrices in AVC/H.264

Intra-Mode Indexed Nonuniform Quantization Parameter Matrices in AVC/H.264 Intra-Mode Indexed Nonuniform Quantization Parameter Matrices in AVC/H.264 Jing Hu and Jerry D. Gibson Department of Electrical and Computer Engineering University of California, Santa Barbara, California

More information

Implementation and analysis of Directional DCT in H.264

Implementation and analysis of Directional DCT in H.264 Implementation and analysis of Directional DCT in H.264 EE 5359 Multimedia Processing Guidance: Dr K R Rao Priyadarshini Anjanappa UTA ID: 1000730236 priyadarshini.anjanappa@mavs.uta.edu Introduction A

More information

Video Compression System for Online Usage Using DCT 1 S.B. Midhun Kumar, 2 Mr.A.Jayakumar M.E 1 UG Student, 2 Associate Professor

Video Compression System for Online Usage Using DCT 1 S.B. Midhun Kumar, 2 Mr.A.Jayakumar M.E 1 UG Student, 2 Associate Professor Video Compression System for Online Usage Using DCT 1 S.B. Midhun Kumar, 2 Mr.A.Jayakumar M.E 1 UG Student, 2 Associate Professor Department Electronics and Communication Engineering IFET College of Engineering

More information

Image Interpolation with Dense Disparity Estimation in Multiview Distributed Video Coding

Image Interpolation with Dense Disparity Estimation in Multiview Distributed Video Coding Image Interpolation with Dense in Multiview Distributed Video Coding Wided Miled, Thomas Maugey, Marco Cagnazzo, Béatrice Pesquet-Popescu Télécom ParisTech, TSI departement, 46 rue Barrault 75634 Paris

More information

Joint Tracking and Multiview Video Compression

Joint Tracking and Multiview Video Compression Joint Tracking and Multiview Video Compression Cha Zhang and Dinei Florêncio Communication and Collaborations Systems Group Microsoft Research, Redmond, WA, USA 98052 {chazhang,dinei}@microsoft.com ABSTRACT

More information

JPEG: An Image Compression System

JPEG: An Image Compression System JPEG: An Image Compression System ISO/IEC DIS 10918-1 ITU-T Recommendation T.81 http://www.jpeg.org/ Nimrod Peleg update: April 2007 Basic Structure Source Image Data Reconstructed Image Data Encoder Compressed

More information

Orientation Filtration to Wavelet Transform for Image Compression Application

Orientation Filtration to Wavelet Transform for Image Compression Application Orientation Filtration to Wavelet Transform for Image Compression Application Shaikh froz Fatima Muneeruddin 1, and Dr. Nagaraj B. Patil 2 Abstract---In the process of image coding, wavelet transformation

More information

Error Resilient Image Transmission over Wireless Fading Channels

Error Resilient Image Transmission over Wireless Fading Channels Error Resilient Image Transmission over Wireless Fading Channels M Padmaja [1] M Kondaiah [2] K Sri Rama Krishna [3] [1] Assistant Professor, Dept of ECE, V R Siddhartha Engineering College, Vijayawada

More information

OPTIMIZED MULTIPLE DESCRIPTION SCALAR QUANTIZATION BASED 3D MESH CODING

OPTIMIZED MULTIPLE DESCRIPTION SCALAR QUANTIZATION BASED 3D MESH CODING OPTIMIZED MULTIPLE DESCRIPTION SCALAR QUANTIZATION BASED 3D MESH CODING M. Oguz Bici 1, Gozde Bozdagi Akar 1, Andrey Norkin 2 and Atanas Gotchev 2 1 Middle East Technical University, Ankara, Turkey 2 Department

More information

LOW DELAY DISTRIBUTED VIDEO CODING. António Tomé. Instituto Superior Técnico Av. Rovisco Pais, Lisboa, Portugal

LOW DELAY DISTRIBUTED VIDEO CODING. António Tomé. Instituto Superior Técnico Av. Rovisco Pais, Lisboa, Portugal LOW DELAY DISTRIBUTED VIDEO CODING António Tomé Instituto Superior Técnico Av. Rovisco Pais, 1049-001 Lisboa, Portugal E-mail: MiguelSFT@gmail.com Abstract Distributed Video Coding (DVC) is a new video

More information

Packed Integer Wavelet Transform Constructed by Lifting Scheme

Packed Integer Wavelet Transform Constructed by Lifting Scheme 1496 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 10, NO. 8, DECEMBER 2000 Packed Integer Wavelet Transform Constructed by Lting Scheme Chengjiang Lin, Bo Zhang, and Yuan F. Zheng

More information

MANY image and video compression standards such as

MANY image and video compression standards such as 696 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL 9, NO 5, AUGUST 1999 An Efficient Method for DCT-Domain Image Resizing with Mixed Field/Frame-Mode Macroblocks Changhoon Yim and

More information

Systematic Lossy Error Protection for Video Transmission over Wireless Ad Hoc Networks

Systematic Lossy Error Protection for Video Transmission over Wireless Ad Hoc Networks Systematic Lossy Error Protection for Transmission over Wireless Ad Hoc Networks Xiaoqing Zhu, Shantanu Rane and Bernd Girod Information Systems Laboratory, Stanford University, Stanford, CA 94305 ABSTRACT

More information

System Modeling and Implementation of MPEG-4. Encoder under Fine-Granular-Scalability Framework

System Modeling and Implementation of MPEG-4. Encoder under Fine-Granular-Scalability Framework System Modeling and Implementation of MPEG-4 Encoder under Fine-Granular-Scalability Framework Final Report Embedded Software Systems Prof. B. L. Evans by Wei Li and Zhenxun Xiao May 8, 2002 Abstract Stream

More information

Robust Video Coding. Heechan Park. Signal and Image Processing Group Computer Science Department University of Warwick. for CS403

Robust Video Coding. Heechan Park. Signal and Image Processing Group Computer Science Department University of Warwick. for CS403 Robust Video Coding for CS403 Heechan Park Signal and Image Processing Group Computer Science Department University of Warwick Standard Video Coding Scalable Video Coding Distributed Video Coding Video

More information

OPTIMIZED QUANTIZATION OF WAVELET SUBBANDS FOR HIGH QUALITY REAL-TIME TEXTURE COMPRESSION. Bob Andries, Jan Lemeire, Adrian Munteanu

OPTIMIZED QUANTIZATION OF WAVELET SUBBANDS FOR HIGH QUALITY REAL-TIME TEXTURE COMPRESSION. Bob Andries, Jan Lemeire, Adrian Munteanu OPTIMIZED QUANTIZATION OF WAVELET SUBBANDS FOR HIGH QUALITY REAL-TIME TEXTURE COMPRESSION Bob Andries, Jan Lemeire, Adrian Munteanu Department of Electronics and Informatics, Vrije Universiteit Brussel

More information

A Fast Scheme for Downsampling and Upsampling in the DCT

A Fast Scheme for Downsampling and Upsampling in the DCT A Fast Scheme for Downsampling and Upsampling in the DCT \ Domain Rakesh Dugad and Narendra Ahuja* Department of Electrical and Computer Engineering Beckman Institute, University of Illinois, Urbana, IL

More information

Context based optimal shape coding

Context based optimal shape coding IEEE Signal Processing Society 1999 Workshop on Multimedia Signal Processing September 13-15, 1999, Copenhagen, Denmark Electronic Proceedings 1999 IEEE Context based optimal shape coding Gerry Melnikov,

More information

LETTER Improvement of JPEG Compression Efficiency Using Information Hiding and Image Restoration

LETTER Improvement of JPEG Compression Efficiency Using Information Hiding and Image Restoration IEICE TRANS. INF. & SYST., VOL.E96 D, NO.5 MAY 2013 1233 LETTER Improvement of JPEG Compression Efficiency Using Information Hiding and Image Restoration Kazumi YAMAWAKI, Member, Fumiya NAKANO, Student

More information

Compression of Light Field Images using Projective 2-D Warping method and Block matching

Compression of Light Field Images using Projective 2-D Warping method and Block matching Compression of Light Field Images using Projective 2-D Warping method and Block matching A project Report for EE 398A Anand Kamat Tarcar Electrical Engineering Stanford University, CA (anandkt@stanford.edu)

More information

Compression of a Binary Source with Side Information Using Parallelly Concatenated Convolutional Codes

Compression of a Binary Source with Side Information Using Parallelly Concatenated Convolutional Codes Compression of a Binary Source with Side Information Using Parallelly Concatenated Convolutional Codes Zhenyu Tu, Jing Li (Tiffany) and Rick S. Blum Electrical and Computer Engineering Department Lehigh

More information

International Journal of Wavelets, Multiresolution and Information Processing c World Scientific Publishing Company

International Journal of Wavelets, Multiresolution and Information Processing c World Scientific Publishing Company International Journal of Wavelets, Multiresolution and Information Processing c World Scientific Publishing Company IMAGE MIRRORING AND ROTATION IN THE WAVELET DOMAIN THEJU JACOB Electrical Engineering

More information

International Journal of Emerging Technology and Advanced Engineering Website: (ISSN , Volume 2, Issue 4, April 2012)

International Journal of Emerging Technology and Advanced Engineering Website:   (ISSN , Volume 2, Issue 4, April 2012) A Technical Analysis Towards Digital Video Compression Rutika Joshi 1, Rajesh Rai 2, Rajesh Nema 3 1 Student, Electronics and Communication Department, NIIST College, Bhopal, 2,3 Prof., Electronics and

More information

A deblocking filter with two separate modes in block-based video coding

A deblocking filter with two separate modes in block-based video coding A deblocing filter with two separate modes in bloc-based video coding Sung Deu Kim Jaeyoun Yi and Jong Beom Ra Dept. of Electrical Engineering Korea Advanced Institute of Science and Technology 7- Kusongdong

More information

Interframe coding A video scene captured as a sequence of frames can be efficiently coded by estimating and compensating for motion between frames pri

Interframe coding A video scene captured as a sequence of frames can be efficiently coded by estimating and compensating for motion between frames pri MPEG MPEG video is broken up into a hierarchy of layer From the top level, the first layer is known as the video sequence layer, and is any self contained bitstream, for example a coded movie. The second

More information

10.2 Video Compression with Motion Compensation 10.4 H H.263

10.2 Video Compression with Motion Compensation 10.4 H H.263 Chapter 10 Basic Video Compression Techniques 10.11 Introduction to Video Compression 10.2 Video Compression with Motion Compensation 10.3 Search for Motion Vectors 10.4 H.261 10.5 H.263 10.6 Further Exploration

More information

Recommended Readings

Recommended Readings Lecture 11: Media Adaptation Scalable Coding, Dealing with Errors Some slides, images were from http://ip.hhi.de/imagecom_g1/savce/index.htm and John G. Apostolopoulos http://www.mit.edu/~6.344/spring2004

More information

Stereo Image Compression

Stereo Image Compression Stereo Image Compression Deepa P. Sundar, Debabrata Sengupta, Divya Elayakumar {deepaps, dsgupta, divyae}@stanford.edu Electrical Engineering, Stanford University, CA. Abstract In this report we describe

More information

An Imperceptible and Blind Watermarking Scheme Based on Wyner-Ziv Video Coding for Wireless Video Sensor Networks

An Imperceptible and Blind Watermarking Scheme Based on Wyner-Ziv Video Coding for Wireless Video Sensor Networks An Imperceptible and Blind Watermarking Scheme Based on Wyner-Ziv Video Coding for Wireless Video Sensor Networks Noreen Imran a,*, Boon-Chong Seet a, A. C. M. Fong b a School of Engineering, Auckland

More information

Network-based model for video packet importance considering both compression artifacts and packet losses

Network-based model for video packet importance considering both compression artifacts and packet losses Network-based model for video packet importance considering both compression artifacts and packet losses Yuxia Wang Communication University of China Beijing, China, 124 Email: yuxiaw@cuc.edu.cn Ting-Lan

More information

Wireless Communication

Wireless Communication Wireless Communication Systems @CS.NCTU Lecture 6: Image Instructor: Kate Ching-Ju Lin ( 林靖茹 ) Chap. 9 of Fundamentals of Multimedia Some reference from http://media.ee.ntu.edu.tw/courses/dvt/15f/ 1 Outline

More information

642 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 11, NO. 5, MAY 2001

642 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 11, NO. 5, MAY 2001 642 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 11, NO. 5, MAY 2001 Transactions Letters Design of Wavelet-Based Image Codec in Memory-Constrained Environment Yiliang Bao and C.-C.

More information

Distributed Source Coding for Image and Video Applications. Acknowledgements

Distributed Source Coding for Image and Video Applications. Acknowledgements Distributed Source oding for Image and Video Applications Ngai-Man (Man) HEUNG Signal and Image Processing Institute University of Southern alifornia http://biron.usc.edu/~ncheung/ 1 Acknowledgements ollaborators

More information

Blind Measurement of Blocking Artifact in Images

Blind Measurement of Blocking Artifact in Images The University of Texas at Austin Department of Electrical and Computer Engineering EE 38K: Multidimensional Digital Signal Processing Course Project Final Report Blind Measurement of Blocking Artifact

More information

Comparative Study of Partial Closed-loop Versus Open-loop Motion Estimation for Coding of HDTV

Comparative Study of Partial Closed-loop Versus Open-loop Motion Estimation for Coding of HDTV Comparative Study of Partial Closed-loop Versus Open-loop Motion Estimation for Coding of HDTV Jeffrey S. McVeigh 1 and Siu-Wai Wu 2 1 Carnegie Mellon University Department of Electrical and Computer Engineering

More information

Module 8: Video Coding Basics Lecture 42: Sub-band coding, Second generation coding, 3D coding. The Lecture Contains: Performance Measures

Module 8: Video Coding Basics Lecture 42: Sub-band coding, Second generation coding, 3D coding. The Lecture Contains: Performance Measures The Lecture Contains: Performance Measures file:///d /...Ganesh%20Rana)/MY%20COURSE_Ganesh%20Rana/Prof.%20Sumana%20Gupta/FINAL%20DVSP/lecture%2042/42_1.htm[12/31/2015 11:57:52 AM] 3) Subband Coding It

More information

Three Dimensional Motion Vectorless Compression

Three Dimensional Motion Vectorless Compression 384 IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.4, April 9 Three Dimensional Motion Vectorless Compression Rohini Nagapadma and Narasimha Kaulgud* Department of E &

More information

Fast Decision of Block size, Prediction Mode and Intra Block for H.264 Intra Prediction EE Gaurav Hansda

Fast Decision of Block size, Prediction Mode and Intra Block for H.264 Intra Prediction EE Gaurav Hansda Fast Decision of Block size, Prediction Mode and Intra Block for H.264 Intra Prediction EE 5359 Gaurav Hansda 1000721849 gaurav.hansda@mavs.uta.edu Outline Introduction to H.264 Current algorithms for

More information

SINGLE PASS DEPENDENT BIT ALLOCATION FOR SPATIAL SCALABILITY CODING OF H.264/SVC

SINGLE PASS DEPENDENT BIT ALLOCATION FOR SPATIAL SCALABILITY CODING OF H.264/SVC SINGLE PASS DEPENDENT BIT ALLOCATION FOR SPATIAL SCALABILITY CODING OF H.264/SVC Randa Atta, Rehab F. Abdel-Kader, and Amera Abd-AlRahem Electrical Engineering Department, Faculty of Engineering, Port

More information

CODING METHOD FOR EMBEDDING AUDIO IN VIDEO STREAM. Harri Sorokin, Jari Koivusaari, Moncef Gabbouj, and Jarmo Takala

CODING METHOD FOR EMBEDDING AUDIO IN VIDEO STREAM. Harri Sorokin, Jari Koivusaari, Moncef Gabbouj, and Jarmo Takala CODING METHOD FOR EMBEDDING AUDIO IN VIDEO STREAM Harri Sorokin, Jari Koivusaari, Moncef Gabbouj, and Jarmo Takala Tampere University of Technology Korkeakoulunkatu 1, 720 Tampere, Finland ABSTRACT In

More information

A Very Low Bit Rate Image Compressor Using Transformed Classified Vector Quantization

A Very Low Bit Rate Image Compressor Using Transformed Classified Vector Quantization Informatica 29 (2005) 335 341 335 A Very Low Bit Rate Image Compressor Using Transformed Classified Vector Quantization Hsien-Wen Tseng Department of Information Management Chaoyang University of Technology

More information

VC 12/13 T16 Video Compression

VC 12/13 T16 Video Compression VC 12/13 T16 Video Compression Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos Miguel Tavares Coimbra Outline The need for compression Types of redundancy

More information

Advanced Encoding Features of the Sencore TXS Transcoder

Advanced Encoding Features of the Sencore TXS Transcoder Advanced Encoding Features of the Sencore TXS Transcoder White Paper November 2011 Page 1 (11) www.sencore.com 1.605.978.4600 Revision 1.0 Document Revision History Date Version Description Author 11/7/2011

More information

JOINT DISPARITY AND MOTION ESTIMATION USING OPTICAL FLOW FOR MULTIVIEW DISTRIBUTED VIDEO CODING

JOINT DISPARITY AND MOTION ESTIMATION USING OPTICAL FLOW FOR MULTIVIEW DISTRIBUTED VIDEO CODING JOINT DISPARITY AND MOTION ESTIMATION USING OPTICAL FLOW FOR MULTIVIEW DISTRIBUTED VIDEO CODING Matteo Salmistraro*, Lars Lau Rakêt, Catarina Brites, João Ascenso, Søren Forchhammer* *DTU Fotonik, Technical

More information

EE 5359 MULTIMEDIA PROCESSING SPRING Final Report IMPLEMENTATION AND ANALYSIS OF DIRECTIONAL DISCRETE COSINE TRANSFORM IN H.

EE 5359 MULTIMEDIA PROCESSING SPRING Final Report IMPLEMENTATION AND ANALYSIS OF DIRECTIONAL DISCRETE COSINE TRANSFORM IN H. EE 5359 MULTIMEDIA PROCESSING SPRING 2011 Final Report IMPLEMENTATION AND ANALYSIS OF DIRECTIONAL DISCRETE COSINE TRANSFORM IN H.264 Under guidance of DR K R RAO DEPARTMENT OF ELECTRICAL ENGINEERING UNIVERSITY

More information