Current Trends and Perspective View of Lossless Video Compression Technique

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1 Volume 1, No. 9, November 2012 ISSN The International Journal of Computer Science & Applications (TIJCSA) RESEARCH PAPER Available Online at Current Trends and Perspective View of Lossless Video Compression Technique Dr. S.K.Mahendran, Director Department of Computer Applications SVS Institute of Computer Applications Coimbatore,India Abstract In video processing, compression techniques are required to improve the efficiency of transmission and storage of video data. Data compression methods can be classified into two basically different categories: lossy and lossless compression methods. Lossless compression methods are required for medical image processing and for compressions broadcast materials. While digital video compression is predominantly lossy, this paper reports algorithms that are designed for lossless video compression. It is an attempt to describe briefly the state of art end prospective directions of development of video compression. The paper deals with coding of video captured from real world and the main aspect considered is lossless coding. Lossless video coding is required in the fields of medical industry, archiving and editing digital cinema or digital broadcasting contents, etc. Keywords-Lossy and lossless compression, video compression, Interframe, Interaframe, etc. 1. Introduction The appearance of inexpensive and powerful processors coupled with fast network access, the continuing expansion of the Internet, and a significant increase in both research and standardisation have all contributed to the infrastructure of modern video/image processing technology [24]. This technology has supported, and continues to enable, a raft of multimedia applications as diverse as home and disaster zone monitoring, video-on-demand, videoconferencing, cellular videophones, remote-sensing, tele-medicine, interactive multimedia databases, multimedia videotex, computer games, 2012, - TIJCSA All Rights Reserved 115

2 multimedia annotation, communication aids for deaf people, security surveillance, and broadcasting and streaming [26]. In video processing, compression techniques are required to improve the efficiency of transmission and storage of video data. These can be classified into two basically different categories: lossy compression methods and lossless compression methods [28]. With lossless compression, each pixel is kept unchanged, resulting in an identical, bit-forbit image after decompression. With lossy compression, the resulting video sequence will be different from the original, but good enough for use. In most cases, a lossy method can produce a much smaller compressed file than any lossless method, while still meeting the requirements of the application. In some cases, like master films or medical imaging, losses in reproduced video are not desirable or acceptable. In such scenarios, the need for having a lossless video compression technique that can achieve high compression ratio while maintaining the original video quality is of great demand. Lossless video compression techniques compress video data efficiently but in a way that the original video data can be restored when needed. State-of-the-art video coding standards, e.g. H.263/4 and MPEG-1/2/4 [15], have been developed primarily for applications where some loss of information without much degradation of the visual quality is acceptable. Many applications such as archiving master copies of digital movies, and capturing medical videos, however, indicate the growing demand and importance of efficient lossless video compression algorithms [1]. The lossless video compression schemes aim to compress video signals with no loss of data, that is, when the video file is compressed in a lossless fashion, 100 percent of the data is still there and compresses it into a smaller space. Lossless video compression is a novel research area, but it is gaining widespread importance. Although research in lossless video compression is limited, a number of attempts were reported in the literature [23][7][22][20][21] working toward removal of both spatial and temporal redundancies. Lossless compression algorithms can be divided in two major categories: prediction based coding and transform based coding. In prediction coding, the pixels of the image are predicted using the spatial correlation, and the residual image (or prediction error image) is entropy coded. If the predictor is accurately designed, the residual image entropy is lower than the original image entropy, and consequently, the compression ratio improves. Transform coding applies a reversible transformation to the image. The resulting image is divided into four or more sub-bands. The sub-band located at the lower frequencies contains most of the signal energy, while the others, at higher frequencies, include a small part of image energy. This kind of coding is typically used when working in lossy-to-lossless mode, as in JPEG2000. This paper is an effort made to understand some of the lossless video coders. In particular, three state-of-the art algorithms are discussed. The remaining part is organized in the following manner. Section 2 describes a hybrid algorithm using interframe and intraframe dependencies to produce a lossless codec. Section 3 modifies H264/AVC, which is a lossy video coder, to be a lossless one, by combining the intraframe with selective pixel-wise prediction. Section 4 explains the use 2012, - TIJCSA All Rights Reserved 116

3 of LAR compression technique to produce a lossless video compression algorithm. Section 5 presents some concluding remarks. 2. Interframe and intraframe lossless video codec Digital video cameras perform a process called demosaicking before compression, which normally is lossy [13][17]. While capturing images, digital video camera, uses only one of the three primary colors (red, green, blue) at each pixel and the remaining colors are interpolated through color demosaicking to reconstruct the full color image [18][14]. A demosaicking algorithm is a digital image process used to reconstruct a full color image from the incomplete color samples output from an image sensor overlaid with a color filter array (CFA). The most commonly used CFA is Bayer Filter [6], where the output is an array of pixel values, each indicating a raw intensity of one of the three filter colors. The process of demosaicking, followed by lossy compression is irreversible and the video cannot be improved later on. Further, this process increases complexity, reduces compression ratio and burdens the camera I/O bandwidth [2]. To overcome these disadvantages, [29] found out that if a lossless compression is performed first in the camera itself before demosaicking, then a sophisticated codec can be designed which is needed in applications where high visual quality has paramount importance. This motivated the use of lossless-compression first followed by demosaicking in medical videos where even the slightest degradation result is catastrophic situations. The algorithm uses a hybrid scheme of inter- and intraframe lossless compression to increase the compression and quality of the medical video data. The working of the encoding algorithm is briefed below and is given pictorially in Figure 1. Figure 1: Encoding Procedure For each input frame to be encoded, the intraframe difference is computed. The interframe distance between the input frame and the previous frame, which serve as the 2012, - TIJCSA All Rights Reserved 117

4 measurements in the adaptive switching between the two coding modes is also calculated. If the interframe distance value is smaller than the intraframe difference value, the previous frame can give a better prediction of the current frame and thus a fast interframe coding technique is used. Otherwise, the interframe prediction accuracy is considered to be lower than the intraframe prediction and then the current frame is encoded by using an intraframe coding method. This hybrid strategy judiciously spends computation. 3. Lossless intra coding based on selective pixel-wise prediction H.264/ AVC (Advanced Video Coder) is an industry standard for video compression, which is the process of converting digital video into a format that takes up less capacity when it is stored or transmitted. Video compression technology can be divided into two basic approaches, Intra (frame-bound) compression, in which all processing is completed within the bounds of each field or frame, and Inter compression, in which the processing is applied across multiple frames. Among the two approaches, intra-frame compression provides the best quality without degrading the video. The H.264/AVC is based on intra-frame and inter-frame coding similar to prior coding standards. The H.264/AVC makes use of quantization and transformation of residual signals produced by intra and inter predictions [27]. For the intra frame coding, intra prediction with previously coded blocks plays an important role in achieving high coding gain. Intra prediction of the H.264/AVC is known to be effective for lossy video coding, however, this is not the case for lossless video coding because intra coding cannot remove all of the inter-pixel redundancy for lossless video coding. Several lossless coding algorithms have been proposed based on prediction and entropy coding [16]. Among existing lossless coding algorithms, the JPEG LS is known to be the most effective for lossless image compression. However, the lossless intra 4:4:4 coding based on the H.264/AVC [6,7] is known to be the most effective among lossless coders supporting video coding functionality. The lossless 4:4:4 coding utilizes inter-pixel prediction along the horizontal or vertical direction according to a selected intra prediction mode. However, it might not be able to remove all the inter-pixel redundancy. To solve this, [26] proposed a lossless intra frame coding method utilizing a pixel-based prediction rather than a block-based prediction. In the proposed algorithm, an additional inter-pixel prediction mode is added to the H.264/AVC s nine intra prediction modes (Figure 2).This option was called the DPCM (Differential Pulse Code Modulation) prediction mode. 2012, - TIJCSA All Rights Reserved 118

5 Figure 2: Nine Intra Prediction Modes The nine prediction modes \generates predictive images based on adjacent blocks of 8 x 8 pixels. By selecting the most suitable predictive mode from among nine luminance signal modes and four color signal modes, it generates accurate predictive images [3]. The working of the algorithm by embedding the DPCM with intra prediction is explained below (Figure 3). 2012, - TIJCSA All Rights Reserved 119

6 Figure 3: Block diagram of Lossless Intra coding with selective pixel-wise prediction system The Selective Residual - DPCM (SR-DPCM) based prediction is a technique that is used by the conventional JOEG lossless coding [3] and is introduced in the present algorithm to remove the inter pixel redundancy and is performed selectively. The encoder transmitted one bit for every 4x4 block or 16x16 block and is used to indicate where the DPCM is used or not. To improve the efficiency of the lossless video coder, the algorithm, along with the DPCM have also used the H2.64 block based prediction at the same time. On experimentation, the results showed that, based on the combined prediction using the inherent coding efficiency of H2.64 intra predictions as well as the inter pixel prediction, the proposed algorithm outperforms the existing standard H.264/AVC FRExt lossless coder. Another advantage found is that the proposed algorithm does not require the transform and quantization process, which normally introduces the lossiness in the coding schemes. 4. Lossless Video Coding Using Locally Adaptive Resolution A Scalable Video Stream [19], Which Is Used by Compression Algorithms to combine several processing levels [8] in order to hierarchically describe the source data, is used by video coders to solve the problem of heterogenousity and time variable data network properties. This section describes a new scalable video coder proposed by [11] that uses a multiresolution approach for compression videos in a lossless fashion. The method is based on a technique called LAR (Locally Adaptive Resolution) [9] that normally works on variable block size decomposition to result in an efficient lossy image compression technique. LAR-APP [5] is a coding technique that is wrought on the LAR method and is used here to produce the lossless part of the video coder developed. LAR video is a low complexity system for low bit-rate color image sequence encoding. It aims to propose a joint solution for coding and representation of the frame content. In particular, it allows to provide a compressed description of both chromatic components and motion information at a region level without region partition encoding. Initially proposed in the LAR coder, used principle has proved to be efficient for still color image encoding. Resulting from hierarchical spatio-temporal segmentation, a Partition Tree (PT) is transmitted to the decoder with a controlled coding cost. The basic principle of LAR is explained in [12]. Three variants, namely, LAR-APP [4], Interleaved S+P [5] and RWHT+P [10] were proposed, which are extension to LAR scheme. The three variants are unified algorithms of compression that combine prediction in an enriched context and scalability in terms of resolution and quality level. These methods take advantage of the two-layer LAR codec and add the multiresolution concept. The pyramidal decomposition is ordered by two successive descent processes. The first decomposition pass in the pyramid refines only small blocks located on contours and models homogeneous areas with larger blocks. The conditional splitting is illustrated by Figure 4 in which block-size devolves from 8 8 to , - TIJCSA All Rights Reserved 120

7 Figure 4: Pyramidal decomposition of LAR image conditionally to the quadtree partition In the first pyramidal decomposition, semantic scalability is realized using grid information. The content-based local information increases the quality of the low bit-rate images which are rebuilt on each level of the pyramid. If the first decomposition intends to reconstruct the low resolution image (LAR-image), the second one processes the local texture information. All blocks at the current level which have not been encoded during the first pass are decomposed by the refinement layer. The scalable DPCM prediction of the LAR-APP method relies on successive descending processes that benefit from context-based information in the causal image (intra-level prediction) and the subsampled image (inter-level prediction). Contrary to LAR-APP, Interleaved S+P and RWHT+P methods work in a transform space which is more suitable to obtain high compression ratio. The main foundation of Interleaved S+P relies on a special implantation of STransform. Originality of this algorithm stands in its feature of efficiently predicting the transformation coefficients from two interleaved pyramids. The third method named RWHT+P, uses a reversible form of the formal Walsh Hadamard Transform applied on 2 2 blocks. The proposed lossless video coder was built on LAR- APP model. Lossless LAR Video Coder In order to remove temporal redundancy between successive frames, both motion estimation and motion compensation are implemented in video coding standards. The common idea is to predict the current image by a motion compensated reference known 2012, - TIJCSA All Rights Reserved 121

8 to the decoder. A regular partition (MPEG-4) or variable block-size decomposition (H.264) is first carried out. Then for each block, motion estimator seeks the best match in a reference image according to a similarity measure. The distance from the best-match block in the reference is represented by a motion vector that is coded and sent to the decoder. The difference between current and compensated image is usually named displaced frame difference (DFD) and has to be efficiently transmitted. In order to remove spatial redundancy, a transform is applied on this residual error. Commonly, tools used by video coding standard supply a robust compression but do not give an acceptable solution in the lossless coding context. Joint use of the video coding scheme and the predictive pyramidal approach aims to meet two fundamental requirements. On one hand, a new algorithm with resolution and quality scalability is proposed. On the other hand, user has at his disposal a simple and unified solution for both lossless and lossy compression. Processing residual error with the LAR-APP method is the solution: a unique algorithm is used to perform in reversible and non-reversible manner (Figure 5). 5. Conclusion Figure 5: Lossless LAR-APP Video Coder In recent years, there have been significant advancements in algorithms and architectures for the processing of image, video and audio signals. These advancements have proceeded along several directions. On the algorithm front, new techniques have led to the development of robust methods to reduce the size of the image, video or audio data (referred to as compression). Such methods are extremely vital in many applications that manipulate and store digital data. Due to the advanced architecture front, it has become feasible to have sophisticated compression algorithms on a relatively low-cost hardware, which, in turn, has spurred a great deal of activity in developing multimedia systems for the large consumer market. Video compression refers to reducing the quantity of data used to represent digital video images, and is a combination of spatial image compression and temporal motion compensation. Lossless compression means that when the video data is decompressed, the result is a bit-for-bit perfect match with the original video 2012, - TIJCSA All Rights Reserved 122

9 stream. This paper presented and discussed the working of three such lossless coders. Over the last decade, a number of researchers engaged themself in the area of lossless video compression. Still, it is often very difficult to find one that best suits for a particular application, especially in the medical field where loss of digital data is undesirable. In future, the application of these algorithms, for medical videos can be studied and the performance can be analyzed. References [1]. Ali, M. (2006) Lossless video coding using distributed source coding techniques, GSIT Workshop on Video/Image Processing, Victoria, Australia. [2]. Ali, M. and Murshed, M. (2006) Lossless Video Coding Using Lattice Based Distributed Source Coding Techniques," avss, IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'06), Pp. 87. [3]. Autori, S. Andriani, G. Calvagno, G.A. Mian (2005) Lossless Video Compression using a Spatio-Temporal Optimal Predictor, Proceedings of the European Signal Processing Conference (EUSIPCO 2005), Antalya, Turkey, Pp.1324:1-4. [4]. Babel, M., Déforges, O. and Ronsin, J. (2003) Lossless and Lossy Minimal Redundancy Pyramidal Decomposition for Scalable Image Compression Technique, IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 03, Vol. III. Hong Kong: Conference cancelled - Invited paper in ICME 2003, Pp [5]. Babel, M., Déforges, O. and Ronsin, J. (2005) Interleaved S+P Pyramidal Decomposition with Refined Prediction Model, IEEE International Conference on Image Processing, ICIP 05, Vol. 2, Genova,Italy, Pp [6]. Bayer, B.E. and Eastman Kodak Company (1975) Color imaging array, US patent [7]. Brunello, D., Calvagno, G., Mian, G.A. and Rinaldo, R. (2003) Lossless compression of video using temporal information, IEEE Trans. Image Process., Vol. 12, No. 2, Pp [8]. Cammas, N. (2004) Codage vidéo scalable par maillages et ondelettes 2D, Ph.D. dissertation, Université de Rennes 1, Rennes. [9]. Déforges, O. and Ronsin, J. (2000) Region of Interest Coding for Low Bit-Rate Image Transmission, Proc. International Conference on Multimedia and Expo ICME 2000, Vol. 1, Pp , - TIJCSA All Rights Reserved 123

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