This paper proposes a No-Reference (NR) Video Quality Assessment (VQA) method for videos subject to the distortion given by High Efficiency Video Coding (HEVC). The proposed assessment can be performed either as a Bitstream-Based (BB) method or as a Pixel-Based (PB). It extracts or estimates the transform coefficients, estimates the distortion, and assesses the video quality. The proposed scheme generates VQA features based on Intra coded frames, and then maps features using an Elastic Net to predict subjective video quality. A set of HEVC coded 4K UHD sequences are tested. Results show that the quality scores computed by the proposed method are highly correlated with the subjective assessment.

This paper considers using BCH codes for distributed source coding using feedback. The focus is on coding using short block lengths for a binary source, X, having a high correlation between each symbol to be coded and a side information, Y, such that the marginal probability of each symbol, Xi in X, given Y is highly skewed. In the analysis, noiseless feedback and noiseless communication are assumed. A rate-adaptive BCH code is presented and applied to distributed source coding. Simulation results for a fixed error probability show that rate-adaptive BCH achieves better performance than LDPCA (Low-Density Parity-Check Accumulate) codes for high correlation between source symbols and the side information.

Distributed video coding (DVC) is a coding paradigm that exploits the source statistics at the decoder side to reduce the complexity at the encoder. The coding efficiency of DVC critically depends on the quality of side information generation and accuracy of noise modeling. This paper considers transform domain Wyner-Ziv (TDWZ) coding and proposes using optical flow to improve side information generation and clustering to improve the noise modeling. The optical flow technique is exploited at the decoder side to compensate for weaknesses of block-based methods, when using motion-compensation to generate side information frames. Clustering is introduced to capture cross band correlation and increase local adaptivity in the noise modeling. This paper also proposes techniques to learn from previously decoded WZ frames. Different techniques are combined by calculating a number of candidate soft side information for low density parity check accumulate decoding. The proposed decoder side techniques for side information and noise learning (SING) are integrated in a TDWZ scheme. On test sequences, the proposed SING codec robustly improves the coding efficiency of TDWZ DVC. For WZ frames using a GOP size of 2, up to 4-dB improvement or an average (Bjøntegaard) bit-rate savings of 37% is achieved compared with DISCOVER.

Distributed video coding (DVC) is an emerging video coding paradigm for systems which fully or partly exploit the source statistics at the decoder to reduce the computational burden at the encoder. This paper considers a Low Density Parity Check (LDPC) based Transform Domain Wyner-Ziv (TDWZ) video codec. To improve the LDPC coding performance in the context of TDWZ, this paper proposes a Wyner-Ziv video codec using bitplane correlation through multiple parallel LDPC decoding. The proposed scheme utilizes inter bitplane correlation to enhance the bitplane decoding performance. Experimental results show that the proposed scheme reduces the bit rate up to 3.9% and improves the rate-distortion (RD) performance of TDWZ.

In recent years, Transform Domain Wyner-Ziv (TDWZ) video coding has been proposed as an efficient Distributed Video Coding (DVC) solution, which fully or partly exploits the source statistics at the decoder to reduce the computational burden at the encoder. In this paper, a parallel iterative LDPC decoding scheme is proposed to improve the coding efficiency of TDWZ video codecs. The proposed parallel iterative LDPC decoding scheme is able to utilize cross bitplane correlation during decoding, by iteratively refining the soft-input, updating a modeled noise distribution and thereafter enhancing the bitplane decoding performance. Experimental results show that the proposed scheme reduces the bit rate of Wyner-Ziv frames up to 5.6% and improves the rate-distortion (RD) performance of TDWZ.

Transform Domain Wyner-Ziv (TDWZ) video coding is an efficient Distributed Video coding solution providing new features such as low complexity encoding, by mainly exploiting the source statistics at the decoder based on the availability of decoder side information. The accuracy of the decoder side information has a major impact on the performance of TDWZ. In this paper, a novel multi-hypothesis based TDWZ video coding is presented to exploit the redundancy between multiple side information and the source information. The decoder used optical flow for side information calculation. Compared with the best available single estimation mode TDWZ, the proposed multi-hypothesis based TDWZ achieves robustly better Rate-Distortion (RD) performance and the overall improvement is up to 0.6 dB at high bitrate and up to 2 dB compared with the DISCOVER TDWZ video codec.

The noise model is one of the most important aspects influencing the coding performance of Distributed Video Coding. This paper proposes a novel noise model for Transform Domain Wyner-Ziv (TDWZ) video coding by using clustering of DCT blocks. The clustering algorithm takes advantage of the residual information of all frequency bands, iteratively classifies blocks into different categories and estimates the noise parameter in each category. The experimental results show that the coding performance of the proposed cluster level noise model is competitive with state-of-the-art coefficient level noise modelling. Furthermore, the proposed cluster level noise model is adaptively combined with a coefficient level noise model in this paper to robustly improve coding performance of TDWZ video codec up to 1.24 dB (by Bjontegaard metric) compared to the DISCOVER TDWZ video codec.

Distributed video coding (DVC) is a novel approach providing new features as low complexity encoding by mainly exploiting the source statistics at the decoder based on the availability of decoder side information. In this paper, scalable-to-lossless DVC is presented based on extending a lossy Transform Domain Wyner-Ziv (TDWZ) distributed video codec with feedback. The lossless coding is obtained by using a reversible integer DCT. Experimental results show that the performance of the proposed scalable-to-lossless TDWZ video codec can outperform alternatives based on the JPEG 2000 standard. The TDWZ codec provides frame by frame encoding. Comparing the lossless coding efficiency, the proposed scalable-to-lossless TDWZ video codec can save up to 5%-13% bits compared to JPEG LS and H.264 Intra frame lossless coding and do so as a scalable-to-lossless coding.

Distributed Video Coding (DVC) is a video coding paradigm which mainly exploits the source statistics at the decoder based on the availability of side information at the decoder. This paper considers feedback channel based Transform Domain Wyner-Ziv (TDWZ) DVC. The coding efficiency of TDWZ video coding does not match that of conventional video coding yet, mainly due to the quality of side information and inaccurate noise estimation. In this context, a novel TDWZ video decoder with noise residue refinement (NRR) and side information refinement (SIR) is proposed. The proposed refinement schemes are successively updating the estimated noise residue for noise modeling and side information frame quality during decoding. Experimental results show that the proposed decoder can improve the Rate- Distortion (RD) performance of a state-of-the-art Wyner-Ziv video codec for the set of test sequences.

Distributed video coding (DVC) has been proposed as a new video coding paradigm to deal with lossy source coding using side information to exploit the statistics at the decoder to reduce computational demands at the encoder. A virtual channel noise model is utilized at the decoder to estimate the noise distribution between the side information frame and the original frame. This is one of the most important aspects influencing the coding performance of DVC. Noise models with different granularity have been proposed. In this paper, an improved noise model for transform domain Wyner-Ziv video coding is proposed, which utilizes cross-band correlation to estimate the Laplacian parameters more accurately. Experimental results show that the proposed noise model can improve the rate-distortion (RD) performance.

Distributed Video Coding (DVC) is a new video coding paradigm which mainly exploits the source statistics at the decoder based on the availability of some decoder side information. The quality of the side information has a major impact on the DVC rate-distortion (RD) performance in the same way the quality of the predictions had a major impact in predictive video coding. In this paper, a DVC solution exploiting multiple side information is proposed; the multiple side information is generated by frame interpolation and frame extrapolation targeting to improve the side information of a single estimation mode. Compared with the best available single side information solutions, the proposed DVC solution with multiple side information robustly improves the RD performance for the set of test sequences.

As a new coding paradigm, distributed video coding (DVC) deals with lossy source coding using side information to exploit the statistics at the decoder to reduce computational demands at the encoder. The performance of DVC highly depends on the quality of side information. With a better side information generation method, fewer bits will be requested from the encoder and more reliable decoded frames will be obtained. In this paper, a side information generation method is introduced to further improve the rate-distortion (RD) performance of transform domain distributed video coding. This algorithm consists of a variable block size based Y, U and V component motion estimation and an adaptive weighted overlapped block motion compensation (OBMC). The proposal is tested and compared with the results of an executable DVC codec released by DISCOVER group (DIStributed COding for Video sERvices). RD improvements on the set of test sequences are observed.

Video compression techniques may yield visually annoying artifacts for limited bitrate coding. In order to improve video quality, a multi-frame based motion compensated filtering algorithm is reported based on combining multiple pictures to form a single super-resolution picture and decimation to the desired format. The algorithm is applied to H.264/AVC decoded sequences and the processing involves a quality estimation based on picture type and local quantization value. Compared with directly decoding, the peak signal to noise ratio (PSNR) of the sequence obtained by the proposed algorithm is improved, and annoying ringing artifacts are effectively suppressed.