The Versatile Video Coding (H.266/VVC) standard has developed by Joint Video Exploration Team (JVET). Compared with the previous generation video coding standard, the H.266/VVC is more outstanding. Since the H.266/VVC introduces multi-type tree (MTT) structure including binary tree (BT) and ternary tree (TT), which brings the significant coding efficiency but increases coding complexity. Moreover, the intra prediction modes have increased from 35 to 67, which can provide more accurate prediction than H.265/High Efficiency Video Coding (HEVC). Therefore, these can improve the encoding quality, but increase computational complexity. To reduce the computational complexity, this paper designs a fast coding unit (CU) partition and intra mode decision algorithm, which includes fast CU partition based on random forest classifier (RFC) model and fast intra prediction modes optimization based on texture region features. Simulation results indicate that the proposed scheme can save 54.91% encoding time with only 0.93% increase in BDBR.

The detection results of many existing co-saliency detection methods are easily interfered by the unrelated salient objects, which have similar appearance characteristics to co-salient objects. Therefore, mining the inter-saliency cues which contain the common category information of multiple related images is the core of co-saliency detection. To address above concern, a novel group weakly supervised learning induced co-saliency detection (GWSCoSal) model is proposed in this paper. First of all, a novel group class activation maps (GCAM) network is constructed and trained through a group weakly supervised learning scheme, which adopts the common category of a group of related images as the ground truth. The GCAM produced by the trained GCAM network are considered as the inter-saliency cues, which can only highlight the regions covered by the objects with common category. Afterwards, the GCAM are integrated into a feature pyramid networks (FPN) based backbone trained by the pixel-level labels to infer the co-saliency maps. The group weakly supervised and the pixel-level learning are jointly implemented for end-to-end training of GWSCoSal model. The comprehensive comparisons with 13 state-of-the-art methods demonstrate that, our GWSCoSal model can detect the co-salient objects more accurately under the condition of being interfered by the similar unrelated salient objects, and the overall performance of which has achieved the level of state-of-the-art methods. The ablation study of our GWSCoSal model validates the effectiveness of proposed GCAM network.

The versatile video coding (VVC) is the latest video coding standard, which uses a Multi-Type Tree (MTT) coding structure. Compared with existing video coding standards, this structure can flexibly split coding blocks according to the complex texture features of the image. As MTT structure introduces binary tree (BT) and ternary tree (TT) splitting, it will lead to a sharp increase in computational complexity. In the paper, a fast Coding Unit (CU) size decision method for intra prediction of VVC is proposed, which can significantly reduce the calculation in the intra encoding of VVC. The proposed method consists of two steps: 1) determine whether CU is divided and 2) select the best CU splitting mode. In the intra prediction process, the CU texture complexity is firstly calculated, which judges whether the CU is divided into sub-CUs. Then, the unnecessary splitting mode candidates are discarded according to the relationship between the texture direction and the CU splitting mode. The experimental results show that our proposed fast CU partition method reduces about 48.58% the computational complexity, while the BDBR only increases by 0.91%.

The joint video expert team (JVET) is currently developing a new video coding standard called H.266/Versatile Video Coding (VVC). Compared with High Efficiency Video Coding (HEVC), VVC has added a variety of coding tools. These tools have greatly improved video compression efficiency and maintained a high level video quality. However, due to the increase in computational complexity, the encoding time is much longer than HEVC. We propose a prediction tool based on DenseNet (a convolutional neural network) to decrease the VVC coding complexity. We predict the probability that the edge of 4 ×4 blocks in each 64 ×64 block is the division boundary by Convolutional Neural Networks (CNN). Then, we skip the unnecessary rate distortion optimization (RDO) and speed up the coding by probability vectors in advance. The proposed method can reduce the coding complexity of 46.10% in VTM10.0 intra coding, while Bjøntegaard delta bit rate (BDBR) only increases by 1.86%. In the sequence with a resolution greater than 1080P, the acceleration efficiency can be at 64.81%, the BDBR loss only increased by 1.92%.

In the current high‐efficiency video coding‐based three‐dimensional video coding (3D‐HEVC) design, new depth intra modes including depth modelling modes and region boundary chain coding are applied for depth map coding. These partition‐based intra modes achieve the highest possible coding efficiency, but result in extremely large encoding time which obstructs the 3D‐HEVC from practical applications. An efficient early termination algorithm for depth map coding in 3D‐HEVC is proposed. It makes use of the coding information from the spatial neighbouring depth map treeblock and the co‐located texture video treeblock to predict the depth map intra mode treeblock and terminate its mode decision process early. Experimental results show that the proposed algorithm can achieve an average computational saving of about 40% with negligible loss of rate distortion performance in the 3D‐HEVC encoder.