Wireless video sensor networks address problems, such as low power consumption of sensor nodes, low computing capacity of nodes, and unstable channel bandwidth. To transmit video of distributed video coding in wireless video sensor networks, we propose an efficient scalable distributed video coding scheme. In this scheme, the scalable Wyner-Ziv frame is based on transmission of different wavelet information, while the Key frame is based on transmission of different residual information. A successive refinement of side information for the Wyner-Ziv and Key frames are proposed in this scheme. Test results show that both the Wyner-Ziv and Key frames have four layers in quality and bit-rate scalable, but no increase in complexity of the encoder.

Single view distributed video coding (DVC) is a coding method that allows for the computational complexity of the system to be shifted from the encoder to the decoder. This property promotes the use of DVC in systems where processing power or energy use at the encoder is constrained. Examples include wireless devices and surveillance. This paper proposes a multi-hypothesis transform domain single-view DVC system that performs symbol level coding with a non-binary low-density parity-check code. The main contributions of the system relate to the methods used for combining multiple side information hypotheses at the decoder. The system also combines interpolation and extrapolation in the side information creation process to improve the performance of the system over larger group-of-picture sizes.

Applying Distributed Video Coding (DVC) to mobile devices that have limited computation and power resources can be a very challenging problem due to its high-complexity decoding. To address this, this paper proposes a DVC bitstream organizer. The proposed DVC bitstream organizer reduces the complexity associated with repetitive channel decoding and SI generation in a flexible manner. It allows users to choose a means of minimizing the computational complexity of the DVC decoder according to their preferences and the device's resource limitations. An experiment shows that the proposed method increases decoding speeds by up to 25 times.

In this paper, side information refinement methods for Wyner-Ziv video codec are presented. In the proposed method, each block of a Wyner-Ziv frame is separated into a predefined number of groups, and these groups are interleaved to be coded. The side information for the first group is generated by the motion compensated temporal interpolation using adjacent key frames only. Then, the side information for remaining groups is gradually refined using the knowledge of the already decoded signal of the current Wyner-Ziv frame. Based on this basic concept, two progressive side information refinement methods are proposed. One is the band-wise side information refinement (BW-SIR) method which is based on transform domain interleaving, while the other is the field-wise side information refinement (FW-SIR) method which is based on pixel domain interleaving. Simulation results show that the proposed methods improve the quality of the side information and rate-distortion performance compared to the conventional side information refinement methods.

We propose an efficient selective block encoding scheme with motion information feedback in distributed video coding (DVC). The proposed scheme estimates the spatial and temporal matching costs for each block in the side information (SI) and for the blocks with high matching costs, the motion information is provided to the encoder side to selectively encode the motion-compensated frame difference signal. Experimental results show that the proposed scheme outperforms the recently developed DVC algorithms.

This letter proposes a method to retrieve the original image X out of multiple sets of SI (Side Information) in distributed video coding (DVC). Using Turbo decoding methods, the most reliable segments from the decoded Yi's were selected for the composition of Y∞, whose conditional entropy H(X| Y∞) became much lower than any individual conditional entropy H(X| Yi). This proposal has improved the peak signal-to-noise ratio (PSNR) by 1.1 to 1.8 dB, compared to the conventional single SI-based approach.

This paper proposes a parallelized DVC framework that treats each bitplane independently to reduce the decoding time. Unfortunately, simple parallelization generates inaccurate bit probabilities because additional side information is not available for the decoding of subsequent bitplanes, which degrades encoding efficiency. Our solution is an effective estimation method that can calculate the bit probability as accurately as possible by index assignment without recourse to side information. Moreover, we improve the coding performance of Rate-Adaptive LDPC (RA-LDPC), which is used in the parallelized DVC framework. This proposal selects a fitting sparse matrix for each bitplane according to the syndrome rate estimation results at the encoder side. Simulations show that our parallelization method reduces the decoding time by up to 35[%] and achieves a bit rate reduction of about 10[%].

Distributed video coding (DVC) technology has been considered to be capable of reducing the processing complexity of the encoder immensely, while majority of the computational overheads are taken over by the decoder. In the common DVC framework, the pictures are decoded using the Wyner-Ziv encoded bit stream received from the encoder and the side information estimated using previously decoded information. As a result, accuracy of the side information estimation is very critical in improving the coding efficiency. In this paper we propose a novel side information refinement technique for DVC using multiple side information streams and sequential motion compensation with luminance and chrominance information involving iterative fusion of parallel information streams. In the bit plane wise coding architecture, previously decoded higher order bit planes are incrementally used to perform the motion estimation jointly in luminance and chrominance spaces to estimate multiple redundant bit streams for iterative fusion to produce more improved side information for subsequent bit planes. Simulation results show significant objective quality gain can be achieved at the same bit rate by utilizing the proposed refinement algorithms.

Distributed Video Coding (DVC) is an emerging video coding paradigm that is characterized by a flexible architecture for designing very low cost video encoders. This feature could be very effectively utilized in a number of potential many-to-one type video coding applications. However, the compression efficiency of the latest DVC implementations still falls behind the state-of-the-art in conventional video coding technologies, namely H.264/AVC. In this paper, a novel non-linear quantization algorithm is proposed for DVC in order to improve the rate-distortion (RD) performance. The proposed solution is expected to exploit the dominant contribution to the picture quality from the relatively small coefficients when the high concentration of the coefficients near zero as evident when the residual input video signal for the Wyner-Ziv frames is considered in the transform domain. The performance of the proposed solution incorporating the non-linear quantizer is compared with the performance of an existing transform domain DVC solution that uses a linear quantizer. The simulation results show a consistently improved RD performance at all bitrates when different test video sequences with varying motion levels are considered.

Wyner and Ziv characterized the rate distortion function for lossy source coding with side information at the decoder. It is well known that for the quadratic Gaussian case, the Wyner-Ziv rate-distortion function coincides with the conditional rate-distortion function. In this paper, we extend the problem to the coding of multivariate Gaussian source with multiple Gaussian side information at the decoder. The achievable region is obtained, and it is easily extended to the case that the difference between the source and the side information is multivariate Gaussian, no matter what distributions the source and the side information are. We apply this theoretical model to Distributed Video Coding (DVC) by considering the difference of the Distributed frame (D frame) and the Side-information frame (S frame) to be multivariate Gaussian distributed. This introduces rate allocation problem into DVC, which can be solved by a reverse water-filling method. Simulation results show that around 1.5-2 dB coding gain benefits from the multivariate Gaussian Wyner-Ziv coding model.

The quality of the Side-information frame (S frame) influences significantly the rate-distortion performance in the Distributed Video Coding (DVC). In this letter, we propose an efficient Side-Information Frame Generator (SIFG). It considers smoothness constraints of both the motion vector field and spatial adjacent pixels. Simulation results show that the proposed techniques provide potential rate-distortion performance advantages. Besides, the fine visual quality of the S frame is obtained.

Distributed Video Coding (DVC), based on the theorems proposed by Slepian-Wolf and Wyner-Ziv, is attracting attention as a new paradigm for video compression. Some of the DVC systems use intra-frame compression based on discrete cosine transform (DCT). Unfortunately, conventional DVC systems have low affinity with DCT. In this paper, we propose a wavelet-based DVC scheme that utilizs current JPEG 2000 standard. Accordingly, the scheme has scalability with regard to resolution and quality. In addition, we propose two methods to increase the coding gain of the new DVC scheme. One is the introduction of a Gray code, and the other method involves optimum quantization. An interesting point is that though our proposed method uses Gray code, it still achieves quality scalability. Tests confirmed that the PSNR is increased about 5 [dB] by the two methods, and the PSNR of the new scheme (with methods) is about 1.5-3 [dB] higher than that of conventional JPEG 2000.

Conventional video compression methods generally require a large amount of computation in the encoding process because they perform motion estimations. In order to reduce the encoding complexity for video compression, this paper proposes a new video compression method based on low-density parity check codes. The proposed method is suitable for resource-constrained devices such as mobile phones and satellite cameras.