The rapid growth of cloud gaming and game streaming has led to a substantial increase in the volume of game content data. To ensure real-time delivery of cloud game content, a common strategy is to downsample and compress the game content before transmission, reducing both data size and bandwidth requirements. However, this approach presents considerable obstacles for super-resolution (SR) networks at the receiver side. In particular, the degraded quality of compressed video streams, combined with the stringent demand for real-time processing, poses major challenges for practical SR applications. In this paper, we propose a novel real-time super-resolution framework that works directly in the compressed domain by exploiting coding-domain priors. Specifically, we propose an extremely lightweight U-Net architecture that leverages prediction maps and residuals as its primary guidance signals. Furthermore, we incorporate the partition map into a Pixel Adaptive Convolution (PAC) module, allowing the convolution kernels to adapt to different regions in the decoded frame. The resulting deep features are then fused with those from the U-Net backbone through an attention block. Finally, we present an enhanced re-parameterization block designed to better model edge features, leading to notable gains in both the objective metrics and subjective visual quality of the reconstructions. Extensive experiments demonstrate that the proposed method consistently outperforms existing real-time approaches on compressed game video content, achieving superior performance in both quality and efficiency.

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.

Neural Network (NN)-based video coding technologies have emerged as a promising alternative to traditional methods, demonstrating significant advantages amidst the rapid advancements in video coding technology. This paper presents a hybrid video coding method based on the Enhanced Compression Model (ECM) developed by the Joint Video Exploration Team (JVET). We integrate two NN-based coding tools into the framework. Specifically, the proposed NN-based Intra Prediction (NNIP) method effectively models the nonlinear relationship between neighboring contextual information and the block to be predicted. The NN-based In-Loop Filtering (NNILF) method adaptively filters the luminance and chrominance components across various quality levels. Experimental results show that the NNIP and NNILF methods achieve 0.56% and 4.14% BD-rate savings for YCbCr components under the All Intra (AI) configuration compared to ECM-14.0. Under the Random Access (RA) configuration, the proposed method can achieve a 2.41% BD-rate saving for YCbCr components.

In this paper, a hybrid video compression framework is proposed that serves as a demonstrative showcase of deep learning-based approaches extending beyond the confines of traditional coding methodologies. The proposed hybrid framework is founded upon the Enhanced Compression Model (ECM), which is a further enhancement of the Versatile Video Coding (VVC) standard. We have augmented the latest ECM reference software with well-designed coding techniques, including block partitioning, deep learning-based loop filter, and the activation of block importance mapping (BIM) which was integrated but previously inactive within ECM, further enhancing coding performance. We evaluate the coding performance of the proposed framework with extensive experiments on the JVET dataset compared with ECM10.0 and VTM-11.0. Due to the testing environment and the coding complexity of the ECM, we did not conduct testing on Class A. The QPs are set as 22, 27, 32, 37, and 42. Compared with ECM-10.0, our method achieves 6.26%, 13.33%, and 12.33% BD-rate savings for the Y, U, and V components under random access (RA) configuration. The traditional hybrid coding framework combined with the three coding tools can further improve compression efficiency and has great potential for performance improvement.

In this article we propose a lightning fast graph embedding method called one-hot graph encoder embedding. It has a linear computational complexity and the capacity to process billions of edges within minutes on standard PC - making it an ideal candidate for huge graph processing. It is applicable to either adjacency matrix or graph Laplacian, and can be viewed as a transformation of the spectral embedding. Under random graph models, the graph encoder embedding is approximately normally distributed per vertex, and asymptotically converges to its mean. We showcase three applications: vertex classification, vertex clustering, and graph bootstrap. In every case, the graph encoder embedding exhibits unrivalled computational advantages.