Nowadays recommender systems (RS) keep drawing attention from academia, and collaborative filtering (CF) is the most successful technique for building RS. To overcome the inherent limitation, which is referred to as data sparsity in CF, various solutions are proposed to incorporate additional social information into recommendation processes, such as trust networks. However, existing methods suffer from multi-source data integration (i.e., fusion of social information and ratings), which is the basis for similarity calculation of user preferences. To this end, we propose a social collaborative filtering method based on novel trust metrics. Firstly, we use Graph Convolutional Networks (GCNs) to learn the associations between social information and user ratings while considering the underlying social network structures. Secondly, we measure the direct-trust values between neighbors by representing multi-source data as user ratings on popular items, and then calculate the indirect-trust values based on trust propagations. Thirdly, we employ all trust values to create a social regularization in user-item rating matrix factorization in order to avoid overfittings. The experiments on real datasets show that our approach outperforms the other state-of-the-art methods on usage of multi-source data to alleviate data sparsity.

Recent deep-learning-based video compression models have demonstrated superior performance over traditional codecs. However, few studies have focused on deep learning rate control. In this paper, end-to-end rate control is proposed for deep contextual video compression (DCVC). With the designed two-branch residual-based network, the optimal bit rate ratio is predicted according to the feature correlation of the adjacent frames. Then, the bit rate can be reasonably allocated for every frame by satisfying the temporal feature. To minimize the rate distortion (RD) cost, the optimal λ of the current frame can be obtained from a two-branch regression-based network using the temporal encoded information. The experimental results show that the achievable BD-rate (PSNR) and BD-rate (SSIM) of the proposed algorithm are -0.84% and -0.35%, respectively, with 2.25% rate control accuracy.

The current high efficiency video coding (HEVC) standard is developed to achieve greatly improved compression performance compared with the previous coding standard H.264/AVC. It adopts a quadtree based picture partition structure to flexibility signal various texture characteristics of images. However, this results in a dramatic increase in computational complexity, which obstructs HEVC in real-time application. To alleviate this problem, we propose a fast coding unit (CU) size decision algorithm in HEVC intra coding based on consideration of the depth level of neighboring CUs, distribution of rate distortion (RD) value and distribution of residual data. Experimental results demonstrate that the proposed algorithm can achieve up to 60% time reduction with negligible RD performance loss.