KEYWORDS: Quantization, Computer programming, Data processing, Algorithm development, Video coding, Information technology, Detection and tracking algorithms
Rate-distortion optimized quantization (RDOQ) is an important technique in the video coding standard, which effectively improves encoding efficiency. However, the large compute complexity and the strong data dependency in the RDOQ calculation process limit the real-time encoding in hardware design. In this paper, a fast RDOQ algorithm is proposed, which includes the RDOQ skip algorithm and the optimized rate estimation algorithm. Firstly, by detecting the Pseudo all-zero block (PZB) in advance, some unnecessary RDOQ processes are skipped, thereby reducing the computational complexity. Secondly, by optimizing the elements used in rate estimation of the RDOQ process, the strong data dependency of the process is alleviated, which allows RDOQ to be executed in parallel. Experimental results show that the proposed algorithm reduces 27.6% and 30.6% encoding time with only average 0.3% and 0.1% BD-rate performance loss under low delay P and random access configurations on the HPM-4.0.1 of AVS3, 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.
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