This paper proposes a highly efficient sample adaptive offset (SAO) mode decision algorithm. By leveraging both the directional correlations between the SAO and intra-prediction decisions, and the SAO decisions' spatial correlations, the SAO mode candidates are effectively pruned during the rate-distortion optimization process, accelerating the SAO encoding process with negligible BD-rate loss.

Block-wise intra/inter prediction, transformation and quantization used in block-based hybrid video coding will inevitably result in blocking artifacts, especially at the low bit rate. To address this problem, this paper employs a deep convolutional neural network (CNN) to approximate the reverse function of video compression, motived by the great success of deep learning in computer vision fields recently. The proposed method establishes an end-to-end mapping, represented as the CNN, which takes the decompressed frame as input and outputs the enhanced one. Employing numerous sequences compressed by H.264 and HEVC reference software, the proposed CNN learns the connections between the lossy frame and the original one in an implicit way under different quantization parameters (QP). Figure 1 shows the architecture of our CNN and the pipeline of the network training. We build our network with convolution layers and ReLU layer and the weights and biases of all the convolution layers in our model are updated by minimizing the loss using stochastic gradient descent with the standard backpropagation. We implement the CNN as a post-loop deblocking filter and explore varying CNN parameters for different QPs. Various experimental results demonstrate that the proposed method is able to significantly improve the quality of enhanced frames in terms of both objective and subjective criterions.

Efficiently exploring the linear relationship among neighboring pixels is a pervasive way to reconstruct high-resolution image from low-resolution one. However, it is a challenge to determine the order of linear model. According to the theory of matrix completion, we propose a single frame super-resolution algorithm by minimizing the sum of all the augmented matrices' rank, which can reflect the order of the region aware linear model. Various experiments demonstrate the images reconstructed by the proposed method have superior PSNR and visual quality, benefitting from its desirable ability of depressing the ringing noise and other artifacts.

Error concealment (EC) is an important post processing technique to deal with the packet loss during the transmission of compressed video stream. This paper aims to address the problem of recovering the missing block in the decoded video stream from the perspective of compressed sensing. The missing block is assumed to be sparsely represented by a dictionary of prototype signal atoms. The atoms are generated by the motion-compensated blocks with a range of motion displacements from the temporally previously reconstructed frame. To avoid inefficient exploration for the prior of sparsity due to the potential coherency among atoms, the regularized least square is incorporated into the compressed sensing reconstruction for the recovery of the missing block. Experimental results demonstrate the superiority of the proposed EC method in terms of objective (PSNR) and subjective quality compared to the existing methods.

Stereo interleaving video coding receives considerable attention due to its desirable property of being compatible with 2D video coding standards. The errors caused by sub sampling (causing distortion between subsampling interpolated image and the original full resolution one) and by quantization during compression lead to the final distortion in stereo interleaving video coding. In this paper, the rate and distortion analysis in stereo interleaving video coding is provided. It proves that appropriate sub sampling in stereo interleaving video coding is able to obtain good compression performance. Subsequently, a content adaptive sub sampling (CAS) is proposed. In CAS, the half resolution frames are generated by decimation, where the down sampling filter coefficients are calculated based on frame contents and the targeted interpolation coefficients. Experiment results demonstrate that the CAS is able to achieve high compression efficiency of stereo interleaving encoding scheme for stereoscopic videos.

Summary form only given. In low bit rate video coding, the frame rate of input sequence can be reduced to the half or even smaller portion by skipping or deleting frames before compression, and then the temporal resolution is restored via up-sampling at the decoder side. Numerous algorithms have been developed to address the problem of temporal resolution improvement. Actually, the quality of up-sampled frames depends on not only the performance of up-sampling method but also the information maintained in the down-sampled video sequence. To improve the quality of up-sampled frames and smooth the quality between the up-sampled and decompressed frames, this paper proposes an up-sampling dependent frame rate reduction, which is shown in Fig. 1. The proposed low bit rate video coding scheme is composed of up-sampling dependent frame rate reduction, compression, decompression and up-sampling components. The proposed frame rate reduction method is hinged to the temporal up-sampling. It is noted that there is a feedback between frame rate reduction and up-sampling in the proposed up-sampling dependent frame rate reduction, of which the goal is to obtain a down-sampled sequence maintaining more information about the frames to be up-sampled at the decoder side.

In this paper, auto regressive (AR) model is applied to error concealment for block-based packet video encoding. Each pixel within the corrupted block is restored as the weighted summation of corresponding pixels within the previous frame in a linear regression manner. Two novel algorithms using weighted least squares method are proposed to derive the AR coefficients. First, we present a coefficient derivation algorithm under the spatial continuity constraint, in which the summation of the weighted square errors within the available neighboring blocks is minimized. The confident weight of each sample is inversely proportional to the distance between the sample and the corrupted block. Second, we provide a coefficient derivation algorithm under the temporal continuity constraint, where the summation of the weighted square errors around the target pixel within the previous frame is minimized. The confident weight of each sample is proportional to the similarity of geometric proximity as well as the intensity gray level. The regression results generated by the two algorithms are then merged to form the ultimate restorations. Various experimental results demonstrate that the proposed error concealment strategy is able to increase the peak signal-to-noise ratio (PSNR) compared to other methods.