Versatile Video Coding (VVC), the state-of-the-art video coding standard, was developed by the Joint Video Experts Team (JVET) of ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG) in 2020. Although VVC can provide powerful coding performance, it requires tremendous computational complexity to determine the optimal mode decision during the encoding process. In particular, VVC adopted the bi-prediction with CU-level weight (BCW) as one of the new tools, which enhanced the coding efficiency of conventional bi-prediction by assigning different weights to the two prediction blocks in the process of inter prediction. In this study, we investigate the statistical characteristics of input features that exhibit a correlation with the BCW and define four useful types of categories to facilitate the inter prediction of VVC. With the investigated input features, a lightweight neural network with multilayer perceptron (MLP) architecture is designed to provide high accuracy and low complexity. We propose a fast BCW mode decision method with a lightweight MLP to reduce the computational complexity of the weighted multiple bi-prediction in the VVC encoder. The experimental results show that the proposed method significantly reduced the BCW encoding complexity by up to 33% with unnoticeable coding loss, compared to the VVC test model (VTM) under the random-access (RA) configuration.

The Moving Picture Experts Group (MPEG) is developing the Video Coding for Machines (VCM) standard to support efficient video compression for machine vision tasks. The VCM standard primarily targets object detection, tracking, and semantic segmentation. Since VCM mainly focuses on object-centric tasks such as detection and tracking, it employs Region-of-Interest (ROI) coding to allocate more bits to foregrounds, while suppressing background regions. This suppression reduces segmentation accuracy by degrading contextual background information. To address this limitation, we propose a luma background restoration method that reconstructs degraded background regions by exploiting the structural correlation between decoded luma and chroma components without relying on complex chroma modeling. The proposed method integrates multi-channel linear modeling with context-based arithmetic coding to efficiently transmit grouped Linear Model (LM) indices for luma restoration. Under VCM test conditions, experimental results show that the proposed method achieves an average Bjøntegaard Delta mean Intersection-over-Union (BD-mIoU) of 7.70, compared with 7.41 achieved by the latest background preservation method. These results demonstrate that the proposed method effectively restores structural background details in luma regions essential for semantic segmentation in VCM frameworks.

Recently, the demand for immersive videos has surged with the expansion of virtual reality, augmented reality, and metaverse technologies. As an international standard, moving picture experts group (MPEG) has developed MPEG immersive video (MIV) to efficiently transmit large-volume immersive videos. The MIV encoder generates atlas videos to convert extensive multi-view videos into low-bitrate formats. When these atlas videos are compressed using conventional video codecs, compression artifacts often appear in the reconstructed atlas videos. To address this issue, this study proposes a feature-extraction-based convolutional neural network (FECNN) to reduce the compression artifacts during MIV atlas video transmission. The proposed FECNN uses quantization parameter (QP) maps and depth information as inputs and consists of shallow feature extraction (SFE) blocks and deep feature extraction (DFE) blocks to utilize layered feature characteristics. Compared to the existing MIV, the proposed method improves the Bjontegaard delta bit-rate (BDBR) by −4.12% and −6.96% in the basic and additional views, respectively.