Recent years have witnessed the exponential increase in the demand for ultra high definition (UHD) video compression. The third generation of Audio Video Coding Standard (AVS3), which is also known as IEEE Standard 1857.10, is the latest audio and video coding standard developed by the China AVS working group. In AVS3, targeting for UHD videos, a series of efficient coding tools have been introduced, leading to the dramatical increase of computational burden. In this scenario, real-time decoding of UHD videos becomes extremely challenging. This paper presents an improved hybrid CPU + GPU accelerated framework for AVS3 decoding. In particular, the motion vector (MV) derivation process is extracted from entropy decoding threads on the CPU. Therefore, the dependency between threads is removed and the entropy decoding can be performed by multiple threads efficiently. Regarding the GPU, we design compact data structures for transform, prediction, and in-loop filtering to reduce the burden of data transmission. A flexible information buffer supporting multi-thread random writing is further created to coordinate the computation between CPU and GPU. Through asynchronous operations on the buffer, the computation of different computing units and the data transmission between them could be performed in parallel. With NVIDIA GeForce RTX 2080Ti GPU and Intel Core i7 8700K CPU, the proposed decoder achieves 151 frames per second (fps) for 4K videos and 55 fps for 8K videos in all intra configuration. In random access configuration, 218 fps and 74 fps are obtained for 4K and 8K videos, respectively.

Contrast distortion has a significant influence on the perceptual quality of an image, which may be generated in various image processing procedures. In this paper, we propose a no-reference image quality assessment (IQA) algorithm for contrast-distorted images based on hybrid features from information and appearance attributes. From information attribute aspect, we utilize the basic information feature to quantify the information of the visible part and the extended information feature further containing the invisible part of an image. From the appearance aspect, we propose an efficient perceptual contrast and colorfulness index to capture the direct visual changes. With the hybrid information attribute and appearance attribute features, support vector regression (SVR) is utilized to learn an IQA model to predict the quality of contrast distorted images. Extensive experimental results on CCID2014 and TID2013 databases further demonstrate the superior performance and robustness of the proposed method.

In this paper, a downlink security-aware resource allocation problem with delay constraint via spectrum sensing is modeled as a mixed integer non-linear problem for non-orthogonal multiple access-based cognitive radio network. The security-aware resource allocation is subject to constraints in required delay for each secondary user, maximum number of accessed secondary users at each subchannel, total interference power threshold introduced to primary users, and total power consumption at secondary BS. The security-aware resource allocation is based on channel state information at the physical layer and queue state information at the link layer. According to the queue buffer occupancy, a probability upper bound of exceeding the maximum packet delay based on M/D/1 queuing model is analyzed in terms of a required minimum secrecy transmission rate. Then, a secondary user scheduling problem and a power allocation problem are solved, separately. Finally, the secondary user scheduling problem is solved via greedy algorithm, and a power allocation algorithm is proposed by successive convex approximation method. The simulation results demonstrate that the performance of proposed algorithms can be improved significantly.