Linbo Qing

Fast inter-mode decision algorithm for high-efficiency video coding based on similarity of coding unit segmentation and partition mode between two temporally adjacent frames

Abstract. High-efficiency video coding (HEVC) introduces a flexible hierarchy of three block structures: coding unit (CU), prediction unit (PU), and transform unit (TU), which have brought about higher coding efficiency than the current national video coding standard H.264/advanced video coding (AVC). HEVC, however, simultaneously requires higher computational complexity than H.264/AVC, although several fast inter-mode decisions were proposed in its development. To further reduce this complexity, a fast inter-mode decision algorithm is proposed based on temporal correlation. Because of the distinct difference of inter-prediction block between HEVC and H.264/AVC, in order to use the temporal correlation to speed up the inter prediction, the correlation of inter-prediction between two adjacent frames needs to be analyzed according to the structure of CU and PU in HEVC. The probabilities of all the partition modes in all sizes of CU and the similarity of CU segmentation and partition modes between two adjacent frames are tested. The correlation of partition modes between two CUs with different sizes in two adjacent frames is tested and analyzed. Based on the characteristics tested and analyzed, at most, two prior partition modes are evaluated for each level of CU, which reduces the number of rate distortion cost calculations. The simulation results show that the proposed algorithm further reduces coding time by 33.0% to 43.3%, with negligible loss in bitrate and peak signal-to-noise ratio, on the basis of the fast inter-mode decision algorithms in current HEVC reference software HM7.0.

Fast Inter-Mode Decision Algorithm for High-Efficiency Video Coding Based on Textural Features

Due to the problems of the new generation of video coding standard HEVC (High-Efficiency Video Coding), such as high computational complexity and the large computation, This paper proposes a fast inter-mode decision algorithm based on image texture features and by using Sobel operator the edge features are extracted from CU which is partitioned by simulation, and then the final partitioning size of CU is determined by the texture features contained in the current CU block of simulation partitioning. By using this method, both of the traverse layers of CU depth and the times of inter predictive coding are reduced. Thus the computational complexity of coding terminal is lowered effectively. The experimental results showed that, compared with inter-mode decision algorithm in HEVC standard, the time of this method is saved 45.35% on average with little loss of coding efficiency and PSNR

Distributed Video Coding with Dynamic Virtual Channel Model Estimation

Distributed video coding (DVC) is a new video coding paradigm, based on Slepian&Wolfs and Wyner&Zivs information theories. A practical case of DVC is Wyner-Ziv codec based on turbo code. One of the most significant works in Wyner-Ziv codec is the estimation of virtual channel model between the side information and the original frame. Better estimation leads to better coding efficiency of DVC. In this paper, the dynamic property of the virtual channel model is discussed in different levels of granularity. A new dynamic virtual channel model estimation algorithm in decoder is proposed. Experimental results show that the proposed algorithm leads to significant improvement in the rate-distortion performance of DVC.

Application of Punctured Turbo Codes in Distributed Video Coding

Distributed video coding (DVC) is a new video coding paradigm derived from Slepian & Wolfs and Wyner & Zivs information-theoretic results in 1970s. Turbo-code-based Wyner-Ziv codec provides a practical solution to lossy DVC schemes. It is able to provide good Rate distortion (DR) performance while shift complexity burden from encoder to decoder. In this paper, we proposed a pixel domain DVC system based on punctured turbo codes. The iterative decoding of turbo codes based on Maximum A Posterior (MAP) for DVC case is elaborated. Virtual channel model estimation is introduced to compute the a priori probabilities to be input to the turbo decoder. The experimental results presents significant gains compared to traditional intraframe coding paradigm. It is concluded that the proposed DVC system obtains better RD performance against previous similar works.

Single Image Super-Resolution via Adaptive Transform-Based Nonlocal Self-Similarity Modeling and Learning-Based Gradient Regularization

Single image super-resolution (SISR) is a challenging work, which aims to recover the missing information in an observed low-resolution (LR) image and generate the corresponding high-resolution (HR) version. As the SISR problem is severely ill-conditioned, effective prior knowledge of HR images is necessary to well pose the HR estimation. In this paper, an effective SISR method is proposed via the local structure-adaptive transform-based nonlocal self-similarity modeling and learning-based gradient regularization (LSNSGR). The LSNSGR exploits both the natural and learned priors of HR images, thus integrating the merits of conventional reconstruction-based and learning-based SISR algorithms. More specifically, on the one hand, we characterize nonlocal self-similarity prior (natural prior) in transform domain by using the designed local structure-adaptive transform; on the other hand, the gradient prior (learned prior) is learned via the jointly optimized regression model. The former prior is effective in suppressing visual artifacts, while the latter performs well in recovering sharp edges and fine structures. By incorporating the two complementary priors into the maximum a posteriori-based reconstruction framework, we optimize a hybrid L1- and L2-regularized minimization problem to achieve an estimation of the desired HR image. Extensive experimental results suggest that the proposed LSNSGR produces better HR estimations than many state-of-the-art works in terms of both perceptual and quantitative evaluations.

Context-Adaptive Modeling for Wavelet-Domain Distributed Video Coding

The authors propose a bit-level context-adaptive correlation model to exploit high-order statistical correlation for wavelet-domain distributed video coding (DVC). The magnitude and sign of each coefficient are coded separately in a bit-plane fashion. The context for magnitude bit plane are designed based on the side information (SI), the local neighborhood, and the parent coefficient. The sign bit plane takes the sign of the SI as the context. The authors also introduce SI binning to classify the SI based on its quality. The SIs class is then included in the contexts for both magnitude coding and sign coding. Experimental results show that the proposed scheme provides significant coding gain over existing DVC systems.

Practical distributed video coding in packet lossy channels

Abstract. Improving error resilience of video communications over packet lossy channels is an important and tough task. We present a framework to optimize the quality of video communications based on distributed video coding (DVC) in practical packet lossy network scenarios. The peculiar characteristics of DVC indeed require a number of adaptations to take full advantage of its intrinsic robustness when dealing with data losses of typical real packet networks. This work proposes a new packetization scheme, an investigation of the best error-correcting codes to use in a noisy environment, a practical rate-allocation mechanism, which minimizes decoder feedback, and an improved side-information generation and reconstruction function. Performance comparisons are presented with respect to a conventional packet video communication using H.264/advanced video coding (AVC). Although currently the H.264/AVC rate-distortion performance in case of no loss is better than state-of-the-art DVC schemes, under practical packet lossy conditions, the proposed techniques provide better performance with respect to an H.264/AVC-based system, especially at high packet loss rates. Thus the error resilience of the proposed DVC scheme is superior to the one provided by H.264/AVC, especially in the case of transmission over packet lossy networks.

Modeling non-stationary correlation noise statistics for Wyner-Ziv video coding

Distributed video coding is a new video coding paradigm, with the ability to shift coding complexity from encoder to decoder. Turbo code based pixel-domain Wyner-Ziv codec is one of the most practical lossy DVC schemes. The performance of Wyner-Ziv codec significantly depends on the modeling of correlation noise statistics between the side information and the original Wyner-Ziv frames. It was observed that the correlation noise statistics differs temporally and spatially. In this paper, a novel algorithm is proposed to model the non-stationary correlation noise statistics. The experiments show that the proposed algorithm has led to significant improvement for Wyner-Ziv coding performance.

Improving distributed video coding by exploiting context-adaptive modeling

The statistical model of the bits to be encoded is crucial for the coding performance of distributed video coding (DVC). In this paper, a bit-level context-adaptive correlation model is proposed to exploit high-order statistical correlation for better channel coding performance, which consequently improves the video coding efficiency. In the proposed scheme, the wavelet domain DVC is considered and the coefficients are coded in a bit-plane fashion. The context for each bit to be coded is first formed. Then the probability distribution of each bit is estimated by using previously available data with the same context. For magnitude coding, the significant state of the following elements are included in the context, (1) the side information, (2) the local neighborhood, (3) the parent coefficients (if applicable). The condition of side information is considered as well. For sign coding, the context consists of the sign and the quality of the side information. The proposed model is implemented within a recently proposed DVC framework with decoderside multi-resolution motion refinement (MRMR). Experimental results show the effectiveness of the proposed scheme with significant coding gain over the original MRMR based DVC system, especially for videos with high motion intensity and for lower bit rates.

Low bit rates image compression via adaptive block downsampling and super resolution

Abstract. A low bit rates image compression framework based on adaptive block downsampling and super resolution (SR) was presented. At the encoder side, the downsampling mode and quantization mode of each 16×16 macroblock are determined adaptively using the ratio distortion optimization method, then the downsampled macroblocks are compressed by the standard JPEG. At the decoder side, the sparse representation-based SR algorithm is applied to recover full resolution macroblocks from decoded blocks. The experimental results show that the proposed framework outperforms the standard JPEG and the state-of-the-art downsampling-based compression methods in terms of both subjective and objective comparisons. Specifically, the peak signal-to-noise ratio gain of the proposed framework over JPEG reaches up to 2 to 4 dB at low bit rates, and the critical bit rate to JPEG is raised to about 2.3 bits per pixel. Moreover, the proposed framework can be extended to other block-based compression schemes.