In light of the growing popularity of screen content video applications, there is a increasing demand for Screen Content Coding (SCC). The latest standard, Versatile Video Coding (VVC), exhibits exceptionally high coding efficiency, albeit accompanied by a considerable coding complexity. This complexity, in turn, restricts the widespread applicability of VVC SCC. To address this issue, this paper introduces a parallel based approach to enhance the coding speed of VVC SCC Intra Coding. Specifically, we established a large-scale database and then design distinct neural networks for Coding Units (CUs) of various sizes to predict candidate Coding Modes (CMs). Subsequently, we formulate a loss function based on CM distributions and Rate Distortion(RD) costs to train the designed models. Finally, we introduce a threshold selection scheme to balance coding efficiency and coding speed. Experimental results demonstrate that the proposed method improves coding speed by an average of 36.36%, with an average increase of 0.95% in Bjøntegaard Delta Bit Rate (BDBR).

360-degree videos (abbreviated as 360 videos) have gained widespread popularity due to their immersive experience. The massive amount of video data resulting from ultra-high resolution of 360 videos makes the coding process extremely complex and seriously limits their widespread applications. In this paper, we propose a new fast-Coding Unit (CU) partition algorithm for 360 videos based on Versatile Video Coding (VVC). The major novelty is that we establish databases and develop models based on stretching and split mode (SM) distribution for 360 videos on VVC. Specifically, first, we establish stretching-based CU partition databases for 360 videos. Second, we propose a new stretching-based multi-scale convolution kernel network structure and a corresponding synthetical loss function, which further considers both Rate-Distortion cost and imbalance issue. We then develop a unique multi-threshold selection scheme to select candidate SMs. Experimental results demonstrate that the proposed algorithm improves encoding speed by 69.63%, with only a 2.28% increase in Bjøntegaard Delta Bit Rate (BDBR), outperforming the state-of-the-art methods.

Intelligent reflective surface (IRS) assisted symbiotic radio (SR) network has been proposed as a promising solution for the sixth generation (6G) mobile wireless system, which achieves mutualistic spectrum sharing and highly reliable backscattering communication with extremely low energy cost. On the other hand, exponential growth in video traffic makes wireless video transmission more challenging in the 6G era. With the assistance of IRS based secondary link in SR network, an efficient soft video transmission scheme (IRSCast) is proposed to achieve linear quality transition under the drastically varying wireless channel. To minimize the transmission distortion of the video signal, a multivariable optimization problem is formulated to jointly optimize the wireless resources, including transmission power, active beamforming of the primary transmitter (PTx), and passive beamforming of the secondary transmitter (STx). Then, an alternating optimization method is utilized to decouple the multivariate optimization problem into multiple univariate sub-problems that are finally solved by semi-positive definite relaxation and Lagrange multiplier methods. The simulation results demonstrated that the proposed IRSCast method significantly improves the objective and subjective quality of the received video.

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by $34.95 \%$ on average while maintaining almost the same coding efficiency.

The CNN-based end-to-end learned image compression methods have already achieved a significant improvement in terms of coding efficiency. Moreover, with the capability of modeling long-range global correlation, the transformer-based image compression has further elevated the coding efficiency to outperform the latest Versatile Video Coding (VVC) standard. Nonetheless, the high computational burden of the self-attention mechanism in the transformer design present a significant obstacle for practical applications. To address this concern, we propose a relatively low-complexity end-to-end learned image compression approach by integrating an efficient lightweight attention module, which effectively mitigates the computational overhead associated with self-attention in the transformer design. The experimental results demonstrate that the proposed method achieves better RD performance than VVC. Furthermore, as compared to the prevailing state-of-the-art transformer-based approach, our method accelerates the coding speed by over 6 times while maintaining comparable coding efficiency.

The compressed video inevitably appears in compression artifacts, which seriously affect the Quality of Experience. The state-of-the-art methods employ deformable alignment to gather similar information from multiple neighborhood frames to enhance target frame quality. However, they always align multiple frames to the target frame simultaneously, which brings repetitive and useless information because of multiple and imperfect alignments. In this paper, we propose a recurrent deformable fusion method which considers the alignment quality distortion caused by time distance from the target frame. Specifically, a Deformable Alignment (DA) module aligns each pair of the target frame and an adjacent frame following the time line. At the same time, a Recurrent Fusion (RF) module integrates the current aligned feature with the previous fused feature. After that, the fused features are concatenated along the time line. Then, a Multi-Scale Attention Reconstruction (MSAR) module is proposed to gather useful information from the fused features. Compared with the previous multi-frame alignment approach, our method can avoid obtaining a lot of repetitive and useless information. Experiment results confirm that our method achieves state-of-the-art performance on the standard test sequences.

R-λ rate control scheme is recommended in the High Efficiency Video Coding (HEVC) standard, which shows high accuracy of bit rate control but lower rate distortion performance. In order to minimize the distortion subject to a target bit rate, a λ domain frame-level rate control scheme for low delay coding of HEVC was proposed. Firstly, an improved parameter updating method is presented for the frame level rate distortion model, which full uses the information of encoded frames in the previous group of pictures (GOP). Then, an adaptive dynamic frame level bit allocation scheme is proposed by employing global rate distortion optimization theory. Finally, to further improve the coding efficiency, the bits allocation adjustment is made for the first several GOPs in video sequence according to the rate distortion dependency. The experimental results show that the proposed method can greatly improve the rate distortion performance under the condition of high accuracy of bit rate control.