KEYWORDS: Distortion, Video coding, Field programmable gate arrays, Computer programming, Digital signal processing, Motion estimation, Computer architecture, Video, Video processing
Skip/direct mode is one of the inter prediction modes in video coding, which achieves a high coding performance. In Audio and Video coding Standard-3(AVS3), skip/direct has improved more performance with more candidate modes. The candidate mode list is generated by numerous prediction directions with corresponded predicted motion vectors. However, it will result in higher computation complexities and challenges to parallel computation, especially for the hardware implementation. For resolving the problem, we propose a hardware architecture of skip/direct mode with a fast motion vector prediction (MVP) algorithm in this paper. Our architecture is designed with efficient pipeline schedules. And the fast MVP algorithm can reduce the number of MVP candidates efficiently. The fast MVP method is introduced by setting a search window, some unnecessary MVP are skipped, thereby reducing the computational complexity firstly. Then the proposed hardware architecture is given with efficient pipeline schedules in detail. The experimental results show that our architecture is able to meet the requirement of 3840x2160@60FPS with only 0.48% and 0.42% BD-Rate increase under the low delay P (LDP) and random access (RA) configurations, respectively.
It’s well known that various extents of discontinuous artifacts often occur in reconstructed video. Massive in-loop coding algorithms have been presented to reduce artifacts. However, when bitrate is insufficient, in-loop coding tools alone fail to solve the problem properly. Preprocessing can be served as an effective solution to reduce compression distortion at low bitrate. In this paper, we propose a novel re-cursively adaptive perceptual non-local means (RAP-NLM) preprocessing algorithm based on just noticeable distortion (JND) model. By recursively employing both spatial and temporal non-local content perceptual characteristics, RAP-NLM filter could be adapted to reduce perceptual redundancies, which will help alleviate the artifacts. Experimental results show that our adaptive perceptual preprocessing algorithm can effectively improve the perceived quality of reconstruction video frames.
The second generation Audio Video Standard (AVS2) adopts the flexible partitioning structure, which recursively divides the coding tree unit (CTU). The prediction models (PM) in inter prediction include PSKIP, P2Nx2N, P2NxN, PNx2N, PHOR_UP, PHOR_DOWN, PVER_LEFT and PVER_RIGHT. Each PM needs to perform integer motion estimation (IME) and fractional motion estimation (FME) in the motion estimation (ME) process. These methods adopted by AVS2 contribute significant coding efficiency while generating great encoding complexity. In order to reduce the encoding complexity, an adaptive inter mode decision fast algorithm is proposed in this paper from two aspects. Firstly, we statistically analyze the PMs in both spatially and temporally adjacent positions and establish a mode complexity (MC) measure metric. Through experiments we know that the complexity of inter mode decision decreases with the reduction of MC. Therefore, MC can be utilized to guide skipping some PMs with low occurrence probability. Secondly, a fast algorithm based on the parent prediction unit (PU) is proposed. FME in child PUs is skipped when the parent PU’s MV from IME is equaled to FME. Experimental results show that, compared to the AVS2 reference software RD17.0, the proposed fast algorithm reduces total encoding time by an average of 21.2% while the performance loss is negligible.
Motion estimation can effectively remove the time-domain redundancy in adjacent frames and greatly reduce the bit rate, so it is widely used in video coding and decoding standards. Motion estimation is also the most computationally intensive and time-consuming module in video coding, so the research on fast motion estimation algorithm has been a hot topic in the field of video coding. In this paper, an innovative parallel fast sub-pixel motion estimation algorithm is proposed for sub-pixel prediction in motion estimation. The algorithm obtains the optimal integer pixel prediction point and 8 adjacent integer pixel points by the integer pixel motion estimation, and uses the 5-parameter quadratic function to fit the residual surface function, and the half-pixel point with the smallest value of the surface function is taken as the optimal 1/2 pixel point. The optimal 1/2 pixel point and an integer pixel at a specific position are taken to infer a suboptimal 1/2 pixel point, and finally search for 12 1/4 pixel points around the optimal 1/2 pixel and sub-optimal 1/2 pixel to determine the optimal 1/4 pixel. Experimental result shows that, under the condition that the image coding quality is basically unchanged, the algorithm can significantly reduce the number of sub-pixel searched points, and effectively reduce the sub-pixel prediction time and computational complexity.
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