Performance Analysis of Video Compression Algorithms for Transmission over Network

Transcription

1 Performance Analysis of Video Compression Algorithms for Transmission over Network Synopsis of the Thesis submitted in Partial Fulfillment of the Requirements for the Degree of Master of Technology in Computer Science and Engineering by Asit Kumar Biswas (Roll No. 06CS6025) Under the supervision of Prof. Jayanta Mukhopadhyay and Prof. Shamik Sural Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur May 2008

2 CONTENTS 1. Introduction Overview Video Compression and Standards Video on Network Overview of the H.264/AVC Video Coding Standard H.264/AVC and its Applications H.264/AVC and Related Work Motivation and Objectives Experimental Procedure Design of Proposed Framework Simulation Environment Analysis of Result Conclusions Bibliography

3 1. Introduction 1.1 Overview The technologies used for telecommunications have changed greatly over the last 50 years. Empowered by research into semiconductors and digital electronics in the telecommunications industry, analog representations of voice, images, and video have been supplanted by digital representations. The biggest consequence has been that all types of media can be represented in the same basic form (i.e. as a stream of bits) and therefore handled uniformly within a common infrastructure (most commonly as Internet Protocol or IP, data streams). There is a technological breakthrough in the areas of digital computation and telecommunication. Particularly exciting has been the participation of the general public in these developments, as affordable computers and the incredible explosion of the World Wide Web have brought a flood of instant information into a large and increasing percentage of homes and businesses. Entrance to the new millennium has further accentuated the trend with the omnipresence of this information through mobile connectivity and diversification in modality of representation - text, graphics, pictures and movies collectively referred as Multimedia. Image and video communication networks are used for a variety of applications such as videoconferencing, broadcast television, and interactive television, video on demand (VoD), multimedia , telemedicine and distance learning. Transmitting video in digital form is the direct result of the benefits offered by digital compression. The potential impact of multimedia information is currently restricted by the bandwidth of the existing communication networks. Along-with the improvement of communication networks to accommodate higher data rates, Quality of Service (QoS) of Networks are of immense importance, specially due to the requirement of synchronization (frame-rate of video must be maintained) of video streams. The bursty nature of compressed video stream is the major hurdle for Network technologies to maintain QoS. 1.2 Video Compression and Standards Compression is a reversible conversion of data to a format that requires fewer bits, usually performed so that the data can be stored or transmitted more efficiently. The size of the data in compressed form (C) relative to the original size (O) is known as the compression ratio (R=C/O). If the inverse of the process, decompression, produces an exact replica of the original data then 1

4 the compression is lossless. Lossy compression, usually applied to image data, does not allow reproduction of an exact replica of the original image, but has a higher compression ratio. Thus lossy compression allows only an approximation of the original to be generated. For image compression, the fidelity of the approximation usually decreases as the compression ratio increases. The success of data compression depends largely on the data itself and some data types are inherently more compressible than others. Generally some elements within the data are more common than others and most compression algorithms exploit this property, known as redundancy. The greater the redundancy within the data, the more successful the compression of the data is likely to be. Fortunately, digital video contains a great deal of redundancy and thus is very suitable for compression. A device (software or hardware) that compresses data is often know as an encoder or coder, whereas a device that decompresses data is known as a decoder. A device that acts as both a coder and decoder is known as a codec. A great number of compression techniques have been developed and some lossless techniques can be applied to any type of data. Development, in recent years, of lossy techniques specifically for image data has contributed a great deal to the realization of digital video applications. Compression techniques used for digital video can be categorized into three main groups: General purpose compression techniques - These can be used for any kind of data. Run Length Encoding (RLE), Relative Encoding, Huffman Coding, Arithmetic Coding are the examples of this type of compression techniques. Intraframe compression techniques - These work on images. Sub-sampling, Coarse Quantization, Vector Quantization, Transform coding are the examples of this type of compression techniques. Interframe compression techniques These work on image sequences rather than individual images. Examples are sub-sampling, Difference Coding, Block Based Difference Coding, Motion Compensation. Video communications requires standardization in order to build interoperable equipments to cater to a variety of requirements. The first standards, H.261, for Personal video telephony was published in

5 International Telecommunication Union (ITU) defined the standards H.261, H.263 for real-time videophone and videoconference application (video only). Joint ISO/IEC Technical Committee developed Audio-visual communication standards MPEG-1/2/ Video on Network Video compression not only reduces the storage requirements or transmission bandwidth of digital video applications, but also affects many system performance tradeoffs. Issues such as bit rate vs. distortion criteria, algorithm complexity, transmission channel characteristics, algorithm symmetry vs. asymmetry, fixed vs. variable rate coding, and standards compatibility should be considered in order to make good compression design decisions. H.264, designed to support a range of applications, including networked and low-bitrate applications, separates the Video Coding Layer (VCL) and a Network Abstraction Layer (NAL). VCL focuses on efficient video compressions, where the NAL formats the coded data for transport over various networks. The network-specific applications adapt and format the NAL packets appropriately for each transport network. The standard also supports several error resilience features: parameter sets, data partitioning, flexible MB ordering (FMO), and flexible slice ordering (FSO). Issues All lossy compression schemes distort and delay the signal. Degradation in source coding mainly comes from the quantization, which is the only irreversible process in a coding scheme. But delays and packet losses are inevitable during transfers across networks. Another issue is complexity. The complexity of the video codec, protocol stacks, and network should also be considered for design of a source codec or channel codec. Complexity is measured in terms of computation, memory capacity (buffer requirement) and memory access requirements. 3

6 2. Overview of the H.264/AVC Video Coding Standard 2.1 H.264/AVC and its Applications Motion Picture Experts Group (MPEG) ISO/IEC JTC 1/SC 29/WG 11 and ITU-T SG16 Q.6 formed Joint Video Team (JVT) for the standardization of H.264/AVC. This was later adopted by ISO/IEC as MPEG-4 Part 10/AVC. It is a general-purpose standard intended for applications ranging from low-bit rate mobile video applications to high-definition TV. Even though H.264 is functionally similar to H.263 and MPEG-2, H.264 introduces several new coding tools that significantly improve coding efficiency. Similar to other hybrid video coding standards, H.264 is a block-based video coding standard where the video is encoded and decoded one macro block (MB). The following are the key improvements in H.264 The H.264 standard extends inter-frame motion-compensated prediction: Variable block sizes for motion compensation. Multi-frame references for prediction, Generalized B frame prediction and use of it as references. Weighted prediction (scaled and offset motion vector), Fractional pixel accuracy for motion vectors. An MB is coded as an intra-mb when temporal prediction is impossible. The prediction for the intra-mb is determined using the neighboring pixels in the same frame. H.264 uses 4 X 4 or 8 X 8 integer transform instead of the 8 X 8 DCT to the prediction residual. The entropy coding in H.264 uses universal variable-length coding (VLC) for all syntax elements and context adaptive VLC (CAVLC) or context-adaptive binary arithmetic coding (CABAC) for quantized coefficients. H.264 applies a deblocking filter to the block edges, except picture boundary and block boundaries for which deblocking is turned off. Network-friendly features: Separating the video coding layer (VCL) and the network abstraction layer (NAL) in the standard makes it network-adaptive. The standard also supports several error resilience features. Since H.264 represents the state of the art in Video Compression, emphasize should be given on H

7 The new standard is designed for technical solutions including at least the following application areas- Broadcast over cable, satellite, cable modem, DSL, terrestrial etc. Interactive or serial storage on optical and magnetic devices, DVD etc. Conversational services over ISDN, Ethernet, LAN, and DSL, wireless and mobile networks, modems etc. or mixtures of these. Video-on-Demand or multimedia streaming services over ISDN, cable modem, DSL, LAN, wireless networks etc. Multimedia messaging services (MMS) over ISDN, DSL, Ethernet, LAN, wireless and mobile networks etc. Moreover, new applications may be deployed over existing and future networks. Hybrid Video Coding In hybrid video coding, video is compressed using a hybrid of motion compensation and transform coding. Temporal domain compression makes use of optical flow models (generally in the form of block-matching Motion Estimation (ME) methods) to identify and mitigate temporal redundancy, whereas Spatial compression (Block Transform like Discrete Cosine Transform (DCT)), Quantization (Q) and Entropy Encoding (EC) operates on a single image block. The feedback path for Motion Compensation is provided by De-quantization (DQ) and Inverse Block Transform (IDCT). Fig 1: Typical Video Encoder Block Diagram 5

8 2.2 H.264/AVC and Related Work H.264/AVC is the current state-of-the-art international video coding standard. It specifies several error resilience tools for efficient delivery in error-prone environments. The partitioning of a picture into slices is very flexible, both in terms of slice sizes and with respect to which macroblocks are allocated to each slice. In addition to simply assigning the macroblocks of a picture to slices in raster scan order, a scheme known as flexible macroblock ordering (FMO) can be used to partition a picture into a number of slice groups using a macroblock allocation map (Ostermann et al., 2004). Pre-defined allocations like e.g. Interleaved and Dispersed slice groups are specified, but the standard also enables explicit allocation of macroblocks to a slice group. Other error resilience tools include arbitrary slice ordering (ASO), data partitioning (DP), and redundant slices (RS). Because the coding mode can be decided on a macroblock level, the standard supports insertion of intra-coded macroblocks into P or B pictures to stop error propagation. The fidelity range extension (FRExt) amendment to H.264/AVC includes new tools and profiles for high-quality consumer and broadcast applications (Sullivan et al., 2004). However, no advanced error resilience tools are allowed in the FRExt profiles, making them less suitable for delivery in error-prone environments unless e.g. forward error correction codes, application level ARQ or a reliable transport protocol are used. RFC 3984 specifies the mapping of NAL units to RTP packets and describes issues related to fragmentation and aggregation of NAL units (Wenger et al., 2005). An H.264 decoder is assumed to be given NAL units in decoding order. In this work only the simple packetization mode is considered, in which no interleaving is used and NAL units are transmitted in decoding order. If the size of the NAL unit is larger than the size of the MTU on the access network, then the NAL unit has to be fragmented. If one fragment is lost, then the entire corresponding NAL unit is corrupted, and has to be discarded. Most of the previously published work on H.264/AVC error robustness has been done using JVT s common test conditions for IP-based transmission (Wenger, 2001) which includes a simple emulator discarding packets based on a loss pattern file. The loss patterns are obtained from Internet experiments, and with a few exceptions mainly consist of scattered packet loss Wenger gave a comprehensive overview of the error- resilience tools in the H.264/AVC standard, and presented results for six different encoder configurations comparing the use of intra macroblock updates, slice partitioning, interleaved FMO, dispersed FMO, and data partitioning 6

9 for two CIF resolution sequences. FMO and data partitioning were concluded to be particularly valuable error resilience tools. Hallbach and Olsen (2004) evaluated the use of motion-sensitive intra macroblock updates, i.e. areas with high motion are more likely to be intra coded, as a means to stop error propagation. Stockhammar et al. (2003) discussed the use of H.264/AVC in a wireless environment and presented an error robustness evaluation for a conversational service with and without feedback using the JVT common test conditions for RTP streaming over 3GPP. While slice partitioning and rate-distortion optimized mode decision give good results without the use of any feedback, excellent results were reported when multiple reference frames and feedback were used to effectively stop error propagation. Calafate et al. (2004) studied the error resilience of H.264/AVC in an ad-hoc wireless network scenario. While previous work to a large extent has focused on low-resolution low-quality applications, this work studies the error robustness of a high-quality application over the network that introduces randomly distributed packet loss. The delay and delay jitter introduced in real packet-switched networks are not considered assuming that the playout buffer can be set large enough so that all delay jitter is absorbed. 3. Motivation and Objectives As more and more telecommunication systems are supporting different kinds of real-time transmission, video transmission being one of the most important applications. The increasing deployment causes the quality of supported video to become a major issue. Many Researchers constrain themselves to prove that the mechanism under study has been able to reduce the packet loss rate, packet delay or packet jitter considering those measures as sufficient to characterize the quality of resulting video transmission. These parameters can not be easily transformed into quality of video transmission. The present work highlights the above requirements and introduces a framework for unified assessment of quality of video transmission and analyzes the performance of Video Compression Algorithms for transmission over network. We aim to design a mechanism and develop a framework for testing H.264 video stream transmission over Network. We also want to test H.264 algorithm and variation for suitability for Network Transmission. 7

10 4. Experimental Procedure 4.1 Design of Proposed Framework. The structure of the framework is shown in the following figure (Fig-2). Fig 2: Proposed Framework The main components of the framework are Encoder, Decoder, Network Simulator and PSNR (Analysis module). Encoder-The Encoder encodes a YUV sequence into a H.264 data format. Here standard JM10.2 will be used. The encoder takes input as YUV sequences and depending on encoding parameters it produces different encoded video streams. The output is compressed video file. Packetizer-The Packetizer module takes encoded video streams as input, fragments each large video frame into smaller segments, and then transmits these segments via UDP packet, the framework records timestamps, the packet id, and the packet pay load in the sender trace file. If the network is simulated, the sender trace file is provided the sender entity of the simulation. This module also generates a video trace file that contains information about every frame in the real video file. The video trace file and sender trace file are later used for subsequent video quality evaluation. Network Simulator- This module takes the sender trace file produced by the encoder module and depending on Network Parameters produce the outputs. It produces two out put files, one is to record the sending time of each packet and other is used to record receive time of each packet. 8

11 Decoder-Based on original encoded video file, the video trace file and sender trace file and receiver trace file, this module creates a frame/packet loss and generates a reconstructed video file, which corresponds to the possibly corrupted video at the receiver side as it would be reproduced to an end user. The generation of the possible corrupted video can be regarded as a process of copying the original video trace file frame by frame, omitting frames indicated at lost or corrupted at receiver side. Finally the decoder decodes to raw YUV format. PSNR (Analysis Module)-PSNR is one of the most widespread objective metrics to assess the application-level QoS of video transmission. PSNR measures the error between a reconstructed image and original one. Prior to transmission, one may then compute a reference PSNR value of the encoded video stream as compared to the original raw video. After transmission, the PSNR is computed at the receiver for the reconstructed video of the possibly corrupted video sequence received. The individual PSNR values at the source or receiver do not mean much, but the difference between the quality of the encoded video at the source and the received one can be used as an objective QoS metric to assess the transmission impact on video quality at the application level. 4.2 Simulation Environment The experimentations were done on H.264 video streams using JM10.2 encoder and decoder. The code was complied using MS Visual C++ on Windows XP (SP2) platform. This can also compile in the Linux. The Packetizer module was compiled using MS Visual C++ on Windows XP (SP2) platform. Network Simulator part is implemented in NS-2.31 on Linux (Fedora 7) platform. Input Sequences Sequence Resolution No of frames Frame Rate foreman qcif (176x144) container qcif (176x144) stefan cif (352x288) football sif (352x240) Table 1: Input Sequences (YUV 4:2:0) used in experiments 9

12 4.3 Analysis of Results The raw video is sent to the encoder and the encoded H.264 streams are received as the output. The encoder only PSNR (PSNR enc ) is calculated also. The value of the configuration parameters for the encoder considered are given below- 1. Input = foreman_qcif.yuv/container_300_qcif/stefan_cif_89.yuv/football_sif_124.yuv 2. StartFrame = 0 3. FrameToBeEncoded = 400/300/90/ OutputFile = test IntraPeriod = NumberReferrenceFrame = NumberBFrames = 0 8. BitRate = 50 to 1200 Kbps 9. InitialQP = 24/26/ BasicUnit = 11 ( No of MBs in the basic unit) The values of the all other encoding parameters are considered default values as in the encoder JM10.2. Experiments were carried out by varying the different parameters for all the four sequences. The results are shown for the foreman sequence only. Bit Rate (Kbps) SNR Y ( db) SNR U ( db) SNRV (db) Table 2: Encoder only PSNR for foreman sequence The value of the configuration parameters for the decoder considered are given below- 1. NAL mode = 0 (AnnexB) 2. Error Concealment =1/2 ( 1: Frame Copy 2: Motion Copy) The values of the all other parameters are considered default values as in the decoder JM10.2. Packet Loss Rate (PLR) has been introduced through Error Model in the Network Simulator and the results were taken for Error Rate 0 to 5 % for the foreman sequence. The results are shown in the tabular form. Error Concealment Frame Copy Motion Vector Copy Mode Data Rate Error Rate % % % % % % % Table 3: Average PSNR for different PLR for foreman sequence 10

13 It has been observed that the Motion Vector Copy Error Concealment method is always better than the Frame Copy Error Concealment method. The following plots have been generated from the above table (Table 3). Data Rate Vs. PSNR Data Rate Vs. PSNR Average PSNR (db ) No Error Error Rate= 0.1% Error Rate= 0.5% Error Rate= 1% Error Rate= 2% Error Rate= 3% Error Rate= 4% Error Rate= 5% Average PSNR (db ) No Error Error Rate= 0.1% Error Rate= 0.5% Error Rate= 1% Error Rate= 2% Error Rate= 3% Error Rate= 4% Error Rate= 5% Data Rate(Kbps) Data Rate(Kbps) Fig 3: Data Rate vs. Average PSNR (Error Concealment: Frame Copy) for foreman Data Rate Vs. PSNR Fig 4: Data Rate vs. Average PSNR (Error Concealment: Motion Copy) for foreman Error Rate vs PSNR EC: Frame Copy EC: Motion Copy Average PSNR(dB ) Frame Copy Motion Copy A v e ra g e P SN R (d B ) Data Rate(Kbps) Fig 5: Data Rate vs. Average PSNR (Error Rate 5%) for foreman Error Rate (% ) Fig 6: Error Rate vs. Average PSNR (Data Rate = 300Kbps) for foreman The MEDIUM5 and ZMMed8 algorithm (Hsin-Ju Feng et al, 2006) have been implemented for error concealment. The lost frame has been marked as B-frame (B-SLICE) and decoder output was observed. Outputs of the decoder are noted in the table for foremen sequence for different PLR (Packet Loss Rate). Error Rate 0 0.1% 0.5% 1.0% 2.0% 3.0% 4.0% 5.0% Methods Frame Copy (FC) Motion Vector Copy (MVC) Mark lost frame as B-SLICE MEDIUM ZMMed MEDIUM5+ZMMed Table 4: Average PSNR for different Error Concealment methods at various PLR for foreman 11

14 Data Rate Vs. PSNR 50 Frame No vs PSNR Average PSNR(dB) Error Rate( %) FC MVC MVC+ Mark as B-SLICE MEDIUM5 ZMMed8 MEDIUM5+ZMMed8 PSNR(dB) Frame No No Drop DROP P1 DROP P2 DROP P3 DROP P4 DROP P5 DROP P6 DROP P7 DROP P8 DROP P9 Fig 7: Average PSNR for different Error Concealment methods at various PLR for foreman Fig 8: Effect in PSNR in the subsequent frames (Football Sequence, GOP 2) due to lost of different P frames The effect of the P Frame drop in the GOP on different sequences is analyzed. The results are shown below. Mark lost frame as B-SLICE method was not considered as it does not give any improvement. Sequence : container_300_qcif.yuv Data Rate : 384 Kbps GOP no : 3 No of frame in GOP: 10(IPPPPPPPPP) Average PSNR of P1 P2 P3 P4 P5 P6 P7 P8 P9 GOP at lost frame no Methods No Loss Frame Copy (FC) Motion Vector Copy MEDIUM ZMMed MEDIUM5+ZMMed Table 5: Average PSNR of GOP 3 (Container) due to lost of different P frames Frame No vs Relative Deviation in PSNR Frame No vs PSNR 4 40 Deviation (FC) Deviation (MVC) 30 Relative Deviation in PSNR( %) 2 Relative Deviation in PSNR( %) Frame No Frame No Fig 9: Frame No vs. Relative Deviation in average Fig 10: Frame No vs. Relative Deviation in average PSNR for different P Frame drop (Average PSNR for different P Frame drop (Average PSNR on total football sequence) PSNR on GOP) Relative Deviation in average is calculated from the given formula (PSNR no loss PSNR P Frame drop ) Relative Deviation in average PSNR = x 100 PSNR no loss 12

15 5. Conclusions The proposed framework can be used to evaluate the performance of network setups or simulations thereof regarding user perceived application quality. Furthermore the calculation of delay and loss is implemented through the NS-2. The framework currently supports H.264/AVC video streaming applications but it can be easily extended to address other video codec(s). A PSNR based quality measurement is introduced which is convenient for shorter as well as longer video sequences. It was successfully tested with Windows and Linux. Slice A type data partitioning is assumed here i.e. one NAL unit consists of a single slice. Whole frame loss is considered due to error induction through Network Simulator. Experiments were done on error concealment algorithms of standard JM 10.2 decoder. Upon the detection of a missing frame, H.264/AVC conceals using either frame copy or motion vector copy algorithm. Concealment using a frame copy method is useful for experiments using fixed frame rate over lossy networks. The frame copy algorithm works well in temporal stationary areas but fails at moving areas. Experiments using the common conditions show that the motion vector copy algorithm outperforms the frame copy algorithm. MEDUIM5 and ZMMed8 (Hsin-Ju Feng et al) methods have been implemented and it is found that decoding performance in terms of PSNR is sometimes better. The frame losses in GOP were analyzed and came to the conclusion that if first P frame is dropped all the concealment methods give the same result. In that case simple frame copy should be used always because of efficiency. For other cases, Motion Copy or ZMMed8 is better. So combination of these methods in a GOP gives better results. The work can be extended for Intra Data Partitioning (Slice-B) & Inter Data Partitioning (Slice-C). For future work, video quality can be further improved by implementing the error concealment in macro block level. 6. Bibliography [1] John B. Smith, H.264 Video Coding Standard, IEEE Computer Society, [2] J. Klaue, B. Rathke, and A. Wolisz, "EvalVid - A Framework for Video Transmission and Quality Evaluation", In Proc. of the 13th International Conference on Modelling Techniques and Tools for Computer Performance Evaluation, pp , Urbana, Illinois, USA, September [3] Frank H.P. Fitzek, Martin Reisslein, "MPEG--4 and H.263 Video Traces for Network Performance Evaluation", IEEE Network, Vol. 15, No. 6, pages 40-54, November/December [4] Barnett, B., Basic Concepts and techniques of Video Coding in Handbook of Image and Video Processing, Editor Bovik, Al, Academic Press, 2000, Chap 6.1, pp

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