Most Wyner-Ziv (WZ) video coding solutions in the literature focus on improving the coding efficiency. Recently, a few papers have addressed problems related to the complexity distribution of WZ video coding by sharing the motion estimation process between the encoder and decoder. However, these methods turn out to significantly increase the computational complexity of the encoding process, due to the presence of motion estimation at the encoder, which is less appealing in WZ coding applications, since their primary requirement is a low-encoding complexity. To address this problem, we propose a different approach to complexity control based on the adaptive selection between WZ and intra coding in the WZ frames. This solution is more flexible than the previous ones in that it can be implemented either at the encoder or decoder. The proposed intra mode selection algorithm exploits the spatial and temporal coherency in the transform domain Wyner-Ziv video coding (TDWZ), in order to achieve complexity control between the WZ encoder and decoder. The experimental results illustrate that the proposed algorithm not only effectively distributes the computational complexity over the encoder and decoder, but also retains the low-complexity feature at the encoder. This should make it attractive for a large number of real video applications of the WZ video coding paradigm. Moreover, the coding efficiency of the conventional TDWZ codec without intra mode decision is improved by up to 2 dB by the proposed intra mode selection algorithm.

Flexible Complexity Control Solution for Transform Domain Wyner-Ziv Video Coding

In this paper, we propose a region-adaptive quantization scheme and progressive side information/channel noise modeling for Wyner-Ziv video coding. The proposed region-adaptive quantization scheme can enhance coding performance and provide an additional functionality of region of interest (ROI) coding. It also improves the accuracy of channel noise modeling by using previously decoded bit-plane information. Simulation results show that the proposed region-adaptive quantization scheme is especially suitable for static sequences, and the progressive side information/channel noise modeling works well in dynamic scenes.

Wyner-Ziv video coding with region adaptive quantization and progressive channel noise modeling

Low complexity distributed video coding

In this paper, we report a fast implementation of Wyner-Ziv video decoder using general-purpose computing on graphics processing units (GPGPU). Despite of its many advantages, Wyner-Ziv video coding has a problem of huge decoding complexity. Since Slepian-Wolf decoding with rate adaptive LDPC accumulate code takes up more than 90% of entire Wyner-Ziv video decoding complexity, in this paper, we focus on fast implementation of the Slepian-Wolf decoder using the CUDA (Compute Unified Device Architecture) which is a GPGPU architecture developed by NVIDIA. Our implementation is shown to be 4∼5 times (QCIF size) or 15∼20 times (CIF size) faster compared to conventional Slepian-Wolf decoding.

Fast implementation of Wyner-Ziv Video codec using GPGPU

This paper proposes an ensemble of multi-layer perceptron (MLP) networks for side information (SI) generation in distributed video coding (DVC). In the proposed scheme, both three-layer and four-layer MLP structures are used to form the ensemble model. The proposed model includes four sub-modules. The first sub-module involves the training of the individual networks. The second sub-module selects ‘M’ number of trained MLPs based on the mean square error (MSE) performance metric. Next, the third sub-module involves the testing phase of each of the selected MLPs. Finally, in the last sub-module, the overall ensemble SI is generated using a dynamically averaging (DA) method. The primary goal of this work is to minimize the estimation error between the SI and the corresponding Wyner-Ziv (WZ) frame so that the overall efficiency of DVC codec can be increased. The proposed scheme is evaluated with respect to different parameters such as Rate-Distortion (RD), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and number of parity requests made per estimated frame. The evaluation indicates that the proposed ensemble model shows better generalization capabilities with improved PSNR (in dB) as compared to each of the individual selected networks. Additionally, the comparative analysis also exhibits that the proposed SI generation scheme generates better SI frames in comparison with the contemporary techniques. Further, using a statistical test, namely, ANOVA with significance level of 5%, it has been validated that the proposed technique yields a significant enhancement in the performance as compared to that of the benchmark schemes.

Decoder driven side information generation using ensemble of MLP networks for distributed video coding

To make an encoder extremely simple by eliminating motion prediction/compensation from encoder, source coding with side information has been investigated based on the Wyner-Ziv theorem as the basic coding principle. However, the frame interpolation at decoder which is essential for redundancy elimination makes erroneous side information when the basic assumption of linear motion between frames is not satisfied. In this paper, we propose a new Wyner-Ziv video coding scheme featuring side matching in the frame interpolation to improve the side information. In the proposed scheme, Wyner-Ziv decoder compensates wrong blocks by side information using side matching and bi-directional searching. The noise reduction in side information allows the proposed algorithm to achieve coding improvements not only in bitrate but also in PSNR. Results of our experiments show improvement of PSNR up to 0.4dB.

https://link.springer.com/content/pdf/10.1007%2F978-3-540-77129-6_69.pdf

Wyner-Ziv video coding with side matching for improved side information

Arising needs for extremely simple encoder motivate investigations on distributed video coding (DVC). The Wyner-Ziv coding, one of the representative DVC schemes, reconstructs video by correcting noise on side information using channel code. Therefore, its coding performance decisively depends on the amount of noise in the side information. However it is hard for a decoder to generate good side information, especially in case that frames are less correlated., Large amount of parity bits certainly helps the decoder to correct noise but it only comes with low compression ratio. In this paper, we propose a method to enhance the quality of the side information, with incrementally transmitted parity bits from encoder. Results of our experiments have verified average PSNR gain up to 0.8 dB.

Wyner-Ziv coding with spatio-temporal refinement based on successive turbo decoding

As a video encoding in resource constrained environments such as sensor networks has become an important issue, DVC(Distributed Video Coding) has been intensively investigated as a solution for light weighted video encoding problem Known as one of the representative schemes of DVC, the Wyner-Ziv coding generates side information of current frame only at decoder, using correlation among frames, and reconstructs video through noise elimination on the side information using channel code Accordingly, the better quality of side information brings less channel noise, thus attains better coding performance of the Wyner-Ziv coder However, since it is hard for decoder to generate an accurate side information without any information of original frame, a method to successively improve side information using successively decoded original frame, based on decoding reliability, was previously developed However, to improve side information from decoding results, not only an error rate of the decoding result as a reliability, but also the amount of reliable information from the decoding result is important Therefore, we propose TDWZ(Transform-domain Wyner-Ziv coding) with successively improving side information based on decoding reliability considering not only an error rate but also the amount of reliable information of the decoding results Our experiment shows the proposed method gains average PSNR up to 17 dB over the previous TDWZ, that is without successive side information improvement

http://koreascience.or.kr:80/article/JAKO200803657006544.pdf

Transform domain Wyner-Ziv video coding with successively improving side information based on decoding reliability

In conventional video coding, encoder complexity is much higher than that of decoder. However, investigations for lightweight encoder to eliminate motion prediction/compensation claiming most complexity in encoder have recently become an important issue. The Wyner-Ziv coding is one of the representative schemes for the problem and, in this scheme, since encoder generates only parity bits of a current frame without performing any type of processes extracting correlation information between frames, it has an extremely simple structure compared to conventional coding techniques. However, in Wyner-Ziv coding, channel decoding errors occur when noisy side information is used in channel decoding process. These channel decoding errors appear more frequently, especially, when there is not enough correlation between frames to generate accurate side information and, as a result, those errors look like Salt & Pepper type noise in the reconstructed frame. Since this noise severely deteriorates subjective video quality even though such noise rarely occurs, previously we proposed a computationally extremely light encoding method based on selective median filter that corrects such noise using spatial correlation of a frame. However, in the previous method, there is a problem that loss of texture from filtering may exceed gain from error correction by the filter for video sequences having complex torture. Therefore, in this paper, we propose an improved lightweight encoding method that minimizes loss of texture detail from filtering by allowing information of texture and that of noise in side information to be utilized by the selective median filter. Our experiments have verified average PSNR gain of up to 0.84dB compared to the previous method.

http://www.ndsl.kr/soc_img/society/ksbe/BSGHC3/2008/v13n2/BSGHC3_2008_v13n2_188.pdf

Lightweight video coding using spatial correlation and symbol-level error-correction channel code

Recently, as the necessity of a light-weighted video encoding technique has been rising for applications such as UCC(User Created Contents) or Multiview Video, Distributed Video Coding(DVC) where a decoder, not an encoder, performs the motion estimation/compensation taking most of computational complexity has been vigorously investigated. Wyner-Ziv coding reconstructs an image by eliminating the noise on side information which is decoder-side prediction of original image using channel code. Generally the side information of Wyner-Ziv coding is generated by using frame interpolation between key frames. The channel code such as Turbo code or LDPC code which shows a performance close to the Shannon`s limit is employed. The noise model of Wyner-Ziv coding for channel decoding is called Virtual Channel Noise and is generally modeled by Laplacian or Gaussian distribution. In this paper, we propose a Wyner-Ziv coding method based on the frequency-adaptive channel noise modeling in transform domain. The experimental results with various sequences prove that the proposed method makes the channel noise model more accurate compared to the conventional scheme, resulting in improvement of the rate-distortion performance by up to 0.52dB.

/pdf/transform-domain-wyner-ziv-coding-based-on-the-frequency-4gnfkbjzlv.pdf

Bong-Hyuck Ko

Papers

Wyner-Ziv video coding with side matching for improved side information

Wyner-Ziv coding with spatio-temporal refinement based on successive turbo decoding

Transform domain Wyner-Ziv video coding with successively improving side information based on decoding reliability

Lightweight video coding using spatial correlation and symbol-level error-correction channel code

Transform domain Wyner-Ziv Coding based on the frequency-adaptive channel noise modeling