The Decoder-side Secondary Transform Derivation (DSTD) method has been adopted by the exploration software of the fourth generation Audio Video coding Standard (AVS4), Exploration Video Model (EVM). Although DSTD can significantly improve the coding performance, it increases the encoding complexity simultaneously. To reduce the encoding complexity of DSTD, three enhanced DSTD methods are proposed in this paper, which consists of Deleting Mode Optimization (DMO), Intra Mode Dependent Optimization (IMDO) and Interleaved Intra Mode Dependent Optimization (IIMDO). The process of DMO is similar to that of the original DSTD method, but all the contents related to diagonal flipping have been removed. IMDO divides intra prediction modes into three areas based on the angle of the intra prediction mode, as shown in Fig. 1. Each area will correspondingly reduce a secondary transform type. IIMDO divides intra prediction modes into four areas, as shown in Fig. 2. The experimental results demonstrate that the proposed methods can effectively reduce the encoding complexity with negligible coding performance loss. The first enhanced method, Deleting Mode Optimization, has been adopted by EVM.

Cloud gaming is an advanced form of Internet service that necessitates local terminals to decode within limited resources and time latency. Super-Resolution (SR) techniques are often employed on these terminals as an efficient way to reduce the required bit-rate bandwidth for cloud gaming. However, insufficient attention has been paid to SR of compressed game video content. Most SR networks amplify block artifacts and ringing effects in decoded frames while ignoring edge details of game content, leading to unsatisfactory reconstruction results. In this paper, we propose a novel lightweight network called Coding Prior-Guided Super-Resolution (CPGSR) to address the SR challenges in compressed game video content. First, we design a Compressed Domain Guided Block (CDGB) to extract features of different depths from coding priors, which are subsequently integrated with features from the U-net backbone. Then, a series of re-parameterization blocks are utilized for reconstruction. Ultimately, inspired by the quantization in video coding, we propose a partitioned focal frequency loss to effectively guide the model's focus on preserving high-frequency information. Extensive experiments demonstrate the advancement of our approach.