English

Distributed Video Coding (DVC) is a new paradigm for video compression based on the information theoretical results of Slepian and Wolf (SW), and Wyner and Ziv (WZ). While conventional coding has a rigid complexity allocation as most of the complex tasks are performed at the encoder side, DVC enables a flexible complexity allocation between the encoder and the decoder. The most novel and interesting case is low complexity encoding and complex decoding, which is the opposite of conventional coding. While the latter is suitable for applications where the cost of the decoder is more critical than the encoder's one, DVC opens the door for a new range of applications where low complexity encoding is required and the decoder's complexity is not critical. This is interesting with the deployment of small and battery-powered multimedia mobile devices all around in our daily life. Further, since DVC operates as a reversed-complexity scheme when compared to conventional coding, DVC also enables the interesting scenario of low complexity encoding and decoding between two ends by transcoding between DVC and conventional coding. More specifically, low complexity encoding is possible by DVC at one end. Then, the resulting stream is decoded and conventionally re-encoded to enable low complexity decoding at the other end. Multiview video is attractive for a wide range of applications such as free viewpoint television, which is a system that allows viewing the scene from a viewpoint chosen by the viewer. Moreover, multiview can be beneficial for monitoring purposes in video surveillance. The increased use of multiview video systems is mainly due to the improvements in video technology and the reduced cost of cameras. While a multiview conventional codec will try to exploit the correlation among the different cameras at the encoder side, DVC allows for separate encoding of correlated video sources. Therefore, DVC requires no communication between the cameras in a multiview scenario. This is an advantage since communication is time consuming (i.e. more delay) and requires complex networking. Another appealing feature of DVC is the fact that it is based on a statistical framework. Moreover, DVC behaves as a natural joint source-channel coding solution. This results in an improved error resilience performance when compared to conventional coding. Further, DVC-based scalable codecs do not require a deterministic knowledge of the lower layers. In other words, the enhancement layers are completely independent from the base layer codec. This is called the codec-independent scalability feature, which offers a high flexibility in the way the various layers are distributed in a network. This thesis addresses the following topics: First, the theoretical foundations of DVC as well as the practical DVC scheme used in this research are presented. The potential applications for DVC are also outlined. DVC-based schemes use conventional coding to compress parts of the data, while the rest is compressed in a distributed fashion. Thus, different conventional codecs are studied in this research as they are compared in terms of compression efficiency for a rich set of sequences. This includes fine tuning the compression parameters such that the best performance is achieved for each codec. Further, DVC tools for improved Side Information (SI) and Error Concealment (EC) are introduced for monoview DVC using a partially decoded frame. The improved SI results in a significant gain in reconstruction quality for video with high activity and motion. This is done by re-estimating the erroneous motion vectors using the partially decoded frame to improve the SI quality. The latter is then used to enhance the reconstruction of the finally decoded frame. Further, the introduced spatio-temporal EC improves the quality of decoded video in the case of erroneously received packets, outperforming both spatial and temporal EC. Moreover, it also outperforms error-concealed conventional coding in different modes. Then, multiview DVC is studied in terms of SI generation, which differentiates it from the monoview case. More specifically, different multiview prediction techniques for SI generation are described and compared in terms of prediction quality, complexity and compression efficiency. Further, a technique for iterative multiview SI is introduced, where the final SI is used in an enhanced reconstruction process. The iterative SI outperforms the other SI generation techniques, especially for high motion video content. Finally, fusion techniques of temporal and inter-view side informations are introduced as well, which improves the performance of multiview DVC over monoview coding. DVC is also used to enable scalability for image and video coding. Since DVC is based on a statistical framework, the base and enhancement layers are completely independent, which is an interesting property called codec-independent scalability. Moreover, the introduced DVC scalable schemes show a good robustness to errors as the quality of decoded video steadily decreases with error rate increase. On the other hand, conventional coding exhibits a cliff effect as the performance drops dramatically after a certain error rate value. Further, the issue of privacy protection is addressed for DVC by transform domain scrambling, which is used to alter regions of interest in video such that the scene is still understood and privacy is preserved as well. The proposed scrambling techniques are shown to provide a good level of security without impairing the performance of the DVC scheme when compared to the one without scrambling. This is particularly attractive for video surveillance scenarios, which is one of the most promising applications for DVC. Finally, a practical DVC demonstrator built during this research is described, where the main requirements as well as the observed limitations are presented. Furthermore, it is defined in a setup being as close as possible to a complete real application scenario. This shows that it is actually possible to implement a complete end-to-end practical DVC system relying only on realistic assumptions. Even though DVC is inferior in terms of compression efficiency to the state of the art conventional coding for the moment, strengths of DVC reside in its good error resilience properties and the codec-independent scalability feature. Therefore, DVC offers promising possibilities for video compression with transmission over error-prone environments requirement as it significantly outperforms conventional coding in this case.

Summary form only given. Distributed video coding (DVC) has been intensively studied in recent years. This new coding paradigm substantially differs from conventional prediction-based video codecs such as MPEG and H.26x, which are characterized by a complex encoder and simple decoder. The conventional DVC codec, e.g., DISCOVER codec, uses advanced frame interpolation techniques to create SI based on adjacent decoded reference frames. The quality of SI is a well-recognized factor in the RD performance of WZ video coding. A high SI quality implies a high correlation between the created SI and the original WZ frame, which then decreases the rate required to achieve a given decoded quality. Clearly, the performance of an SI creation process based on adjacent previously decoded frames is limited by the quality of the past and the future reference frames as well as the distance and motion behavior between them. The correlation between high-motion frames is low and vice versa. That is, SI quality in the conventional codecs depends on the temporal correlation of key frames, which affects the bitrate and PSNR of the compression process. In this work, a novel DVC architecture for dealing with the cases of high-motion and large GOP-size sequences is proposed to better the rate-distortion (RD) performance. For high-motion video sequences, the proposed architecture generates SI by using subsampled spatial information instead of interpolated temporal information. the proposed approach separates the video sequence into subsampled key frames and corresponding WZ frames, which changes the creation of SI. That is, all successive frames on the encoder side are downsized to sub-frames, which are then compressed by an H.264/AVC intra encoder. Experimental results reveal that the subsampling input based DVC codec can gain up to 1.47 dB in the RD measures and maintains the most important characteristic of the DVC codec, the encoder is lightweight, as compared with the conventional WZ codec, respectively. The novel DVC architecture evaluated in this study exploits spatial relations to create SI. The experimental results confirm that the RD performance of the proposed approach is superior to that of the conventional one for high-motion and/or large GOP-size sequences. The quality of spatial interpolation based SI is higher than that of the temporal interpolation one, which leads to a high-PSNR reconstructed WZ frame. The subsampled key frames are also decoded by LDPCA decoder to recover the information lost when H.264/AVC intra coding is used to increase PSNR gain. Since many spatial domain interpolation and super resolution schemes have been proposed for use in the fields of image processing and computer vision, the performance of the proposed DVC codec can be further enhanced by using better schemes to generate even better SI.

Distributed video coding (DVC) architecture designs, based on distributed source coding principles, have benefitted from significant progresses lately, notably in terms of achievable rate-distortion performances. However, a significant performance gap still remains when compared to prediction-based video coding schemes such as H.264/AVC. This is mainly due to the non-ideal exploitation of the video sequence temporal correlation properties during the generation of side information (SI). In fact, the decoder side motion estimation provides only an approximation of the true motion. In this paper, a progressive DVC architecture is proposed, which exploits the spatial correlation of the video frames to improve the motion-compensated temporal interpolation (MCTI). Specifically, Wyner-Ziv (WZ) frames are divided into several spatially correlated groups that are then sent progressively to the receiver. SI refinement (SIR) is performed as long as these groups are being decoded, thus providing more accurate SI for the next groups. It is shown that the proposed progressive SIR method leads to significant improvements over the Discover DVC codec as well as other SIR schemes recently introduced in the literature.

Distributed video coding (DVC) has been featured by exploiting the video statistics, partially or totally at the decoder. Wireless sensor networks are supposed to have lesser complexity encoders at the expense of higher decoder complexity. Therefore DVC is more suitable to video transmission over wireless sensor networks compared to conventional video coding. Current research work on DVC is conducted for lossless channel, i.e, parity bit stream is not influenced by noise or distortion and further correlation noise due to the residual between input video frame and side information is not estimated effectively. In other words , noisy environment is not analyzed with DVC codec in recent research works. In this paper, DVC codec is enabled with the effect of AWGN noise and further a single wireless fading channel (SISO) is considered. The correlation noise is analyzed for Foreman and Carphone video sequences and relationship of correlation of adjacent key frames are discussed.

Distributed Video Coding (DVC) is a new compression paradigm in video coding, which is receiving lots of interests nowadays. Side information creation is a key function in the DVC decoder, and plays a key-role in determining the performance of the codec. This paper proposes a novel framework to create side information at the block level with two modes: Wyner-Ziv mode and Intra/WZ mode, corresponding to Motion Compensated Frame Interpolation and motion compensated quality enhancement technique, respectively. While in the second mode, a low quality Intra block sent by the encoder is used to generate side information by doing motion estimation with the help of the reference frames. Experimental results demonstrate that the proposed scheme can achieve up to 2.1dB improvement in rate-distortion performance when compared to state-of-the-art DVC codec, especially for high motion video sequences and long group of pictures sizes.

Distributed video coding (DVC) is a new video coding paradigm, which allows a reverse complexity codec in which the complexity is shifted from the encoder toward the decoder. Many DVC implementations have to make a prediction of the encoded frame at the decoder side using only the other frames as reference, in a process called side information (SI) generation. This process is crucial to any DVC framework a better SI will lead to a better rate distortion performance. In this work, we propose a linear motion mode to produce SI associated with, and a way to deal with the common problems of this model. Results are presented to support the proposed method. Keywords—Side Information, Wyner-Ziv Video Coding, Motion Compensation Frame Interpolation.

Distributed video coding (DVC) is an emerging video coding paradigm due to its flexibility to introduce much lower encoding complexity than conventional predictive codecs, which is beneficial for some applications such as wireless video surveillance, wireless sensor networks, and disposable video cameras. Although DVC systems without feedback channel address a wider range of applications, it is not commonly discussed in literatures due to its lower coding performance. In this paper, on the basis of PRISM DVC architecture, a low-complexity feedback-channel-free DVC system is proposed with a new classifier to improve the coding performance. Experimental results show that the proposed system can provide about 0.8 dB gain in PSNR to the state-of-the-art and 2 dB gain to IST-PRISM. It is also competitive regarding other feedback-channel-free DVC systems and can even achieve similar performance to DVC systems with feedback channel for low-motion sequences.

Distributed Video Coding (DVC) is a new video coding scheme that mainly uses the availability of decoder side information to reduce the complexity of the encoder. The quality of the side information has an important influence on the performance of the distributed video coding system. At present, the generation of side information is mainly based on a model that uses inter-frame correlation for motion estimation. In order to make full use of the intra-frame self-similarity of the side information frame, we use the idea of image denoising, and propose an improved side information generation method. Since the initial side information frame can be viewed as a noisy version of the Wyner-Ziv (WZ) frame, improving the side information frame can be translated to denoising the frame. The experimental results show that the quality of the side information obtained by the proposed scheme has been significantly improved, thereby effectively improve the performance of distributed video coding.

Mobile 2.0 is being considered as a natural extension of the Web 2.0 revolution to the mobile networks. One important concept in Web 2.0 is the real-time creation and uplink transmission of rich multimedia contents from individual clients to the Internet. However, due to power constraints of mobile handsets, it is difficult to turn this concept into practice in mobile ecosystems. To realize this vision, distributed video coding (DVC) is proposed in 2002 as a possible solution to the design of low complexity video encoders without sacrificing video quality. DVC relies on highly complex decoders to decode-and-restore high quality video output. However, so far, the coding quality of existing DVC codecs is no where near that of the traditional closed-loop codecs. In this paper, an optical flow-based low complexity motion model analysis process is proposed to aid the generation of side information for DVC decoders. Fast total least-squares analysis is performed on the video data at the encoder so that coding decision can be chosen to provide more useful hints for the generation of high quality side information at the decoder. As shown from the experiments, the proposed technique is very promising for improving the quality of DVC codecs and for mobile 2.0 applications.

Distributed video coding presents a viable solution for power-constrained multimedia communication. However, its relatively low coding efficiency compared to the conventional video coding schemes remains a challenging issue. The rate-distortion performance of distributed video coding is highly dependent on the quality of side information generated at the decoder and various techniques have been proposed to improve the side information quality in block-based and frame-based distributed video coding architectures. In this paper, a robust spatiotemporal joint bilateral upsampling based side information generation method is proposed. The proposed side information generation method is based on a block-based low-complexity distributed video coding architecture with adaptive block coding mode classification. A partially reconstructed Wyner-Ziv (WZ) frame with skip and key blocks is downsampled and spatiotemporal error concealment and joint bilateral upsampling are used to generate the side information. Simulation results show that the proposed method improves the quality of side information significantly while keeping low computational complexity.

Perceptual-based distributed video coding

Low-density Parity-Check (LDPC) code is widely used in distributed video coding (DVC) to realizing Wyner-Ziv (WZ) coding for its better performance than other channel codes. However, during the LDPC decoding process, most of DVC solutions do not make use of the video character to help decoding. In this paper, we propose an improved LDPC decoding scheme for DVC by utilizing the movement of the objects in the video sequence to partition different confidence areas for the side information (SI). The pixels in the SI correspond to different confidence areas, and the corresponding binary representations of the pixels will have different likelihood-ratio (LLR) initialization methods which are used to generate the LLR values for LDPC iterative decoding. The experimental results show that with the proposed decoding scheme the PSNR gain can reach up to 0.8 db without increasing the decoding complexity.

Distributed Video Coding (DVC) is a new coding paradigm based on two major information theory results namely the Slepian-Wolf (1973) and Wyner-Ziv(1976) theorems. Slepian-wolf coding is the lossless source coding and Wyner-Ziv coding is the lossy coding with receiver side information. Distributed Video Coding allows the implementation of lightweight encoders and complex decoders. This is the dual to conventional video coding such as MPEG or AVC/H.264 where the encoder has to carry most of the computational burden. There are various practical solutions proposed to address the two areas where DVC is applicable. These areas are low complexity video coding and robust video coding. These solutions are compared based on techniques used for compression, rate control, decoding and motion estimation.

Error-resilient scalable compression based on distributed video coding

Distributed video coding (DVC) suits the light weight encoder and complex decoders, shifting the motion estimation to the decoder, in contrast to the conventional encoding scheme. Mode decision is one of the factor affecting the Rate Distortion (RD) performance due to the varying statistics of the sequence in the DVC. In this paper, we propose to introduce coding modes and the Side Information (SI) refinement modes at the decoder in the pixel domain multi resolution refinement(MRR) Wyner-Ziv scheme. During the decoding process, the mode decision of the current decoding layer of the block is based on the information from the previous decoded layers of the block and the updated SI. We further propose the adaptive weighted reconstruction scheme using the neighbor pixels. RD performance of the proposed scheme is tested with several sequences. The results shows significant performance improvement.