Just‐noticeable‐distortion‐based fast coding unit size decision algorithm for high efficiency video coding

2014 ◽  
Vol 50 (6) ◽  
pp. 443-444 ◽  
Author(s):  
Wei Wu ◽  
Bin Song
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Unit Size ◽  
High Efficiency Video Coding ◽  
Decision Algorithm ◽  
Just Noticeable Distortion ◽  
Coding Unit

A Fast Mode Decision Algorithm for Intra Prediction in High Efficiency Video Coding

2020 ◽  
Vol 10 (2) ◽  
pp. 496-501
Author(s):  
Wen Si ◽  
Qian Zhang ◽  
Zhengcheng Shi ◽  
Bin Wang ◽  
Tao Yan ◽  
...  
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Rate Increase ◽  
Intra Prediction ◽  
High Efficiency Video Coding ◽  
Candidate List ◽  
Decision Algorithm ◽  
Coding Efficiency ◽  
Gradient Based ◽  
Coding Unit

High Efficiency Video Coding (HEVC) is the next generation video coding standard. In HEVC, 35 intra prediction modes are defined to improve coding efficiency, which result in huge computational complexity, as a large number of prediction modes and a flexible coding unit (CU) structure is adopted in CU coding. To reduce this computational burden, this paper presents a gradient-based candidate list clipping algorithm for Intra mode prediction. Experimental results show that the proposed algorithm can reduce 29.16% total encoding time with just 1.34% BD-rate increase and –0.07 dB decrease of BD-PSNR.

scholarly journals An Effective Transform Unit Size Decision Method for High Efficiency Video Coding

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Chou-Chen Wang ◽  
Chi-Wei Tung ◽  
Jing-Wein Wang
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Early Stage ◽  
Test Model ◽  
Unit Size ◽  
High Efficiency Video Coding ◽  
Active Motion ◽  
Decision Algorithm ◽  
Compression Performance ◽  
Dct Coefficients

High efficiency video coding (HEVC) is the latest video coding standard. HEVC can achieve higher compression performance than previous standards, such as MPEG-4, H.263, and H.264/AVC. However, HEVC requires enormous computational complexity in encoding process due to quadtree structure. In order to reduce the computational burden of HEVC encoder, an early transform unit (TU) decision algorithm (ETDA) is adopted to pruning the residual quadtree (RQT) at early stage based on the number of nonzero DCT coefficients (called NNZ-EDTA) to accelerate the encoding process. However, the NNZ-ETDA cannot effectively reduce the computational load for sequences with active motion or rich texture. Therefore, in order to further improve the performance of NNZ-ETDA, we propose an adaptive RQT-depth decision for NNZ-ETDA (called ARD-NNZ-ETDA) by exploiting the characteristics of high temporal-spatial correlation that exist in nature video sequences. Simulation results show that the proposed method can achieve time improving ratio (TIR) about 61.26%~81.48% when compared to the HEVC test model 8.1 (HM 8.1) with insignificant loss of image quality. Compared with the NNZ-ETDA, the proposed method can further achieve an average TIR about 8.29%~17.92%.

Run-Time Deep Learning Enhanced Fast Coding Unit Decision for High Efficiency Video Coding

2019 ◽  
Vol 29 (03) ◽  
pp. 2050046
Author(s):  
Xin Li ◽  
Na Gong
Keyword(s):  
Deep Learning ◽  
Video Coding ◽  
High Efficiency ◽  
Random Access ◽  
High Efficiency Video Coding ◽  
Decision Algorithm ◽  
Coding Efficiency ◽  
Low Delay ◽  
Cu Partition ◽  
Coding Unit

The state-of-the-art high efficiency video coding (HEVC/H.265) adopts the hierarchical quadtree-structured coding unit (CU) to enhance the coding efficiency. However, the computational complexity significantly increases because of the exhaustive rate-distortion (RD) optimization process to obtain the optimal coding tree unit (CTU) partition. In this paper, we propose a fast CU size decision algorithm to reduce the heavy computational burden in the encoding process. In order to achieve this, the CU splitting process is modeled as a three-stage binary classification problem according to the CU size from [Formula: see text], [Formula: see text] to [Formula: see text]. In each CU partition stage, a deep learning approach is applied. Appropriate and efficient features for training the deep learning models are extracted from spatial and pixel domains to eliminate the dependency on video content as well as on encoding configurations. Furthermore, the deep learning framework is built as a third-party library and embedded into the HEVC simulator to speed up the process. The experiment results show the proposed algorithm can achieve significant complexity reduction and it can reduce the encoding time by 49.65%(Low Delay) and 48.81% (Random Access) on average compared with the traditional HEVC encoders with a negligible degradation (2.78% loss in BDBR, 0.145[Formula: see text]dB loss in BDPSNR for Low Delay, and 2.68% loss in BDBR, 0.128[Formula: see text]dB loss in BDPSNR for Random Access) in the coding efficiency.

A Fast Intra Coding Unit Size Decision Based on Statistical Learning for HEVC

2013 ◽  
Vol 303-306 ◽  
pp. 2107-2111 ◽  
Author(s):  
Cheng Tao Zhou ◽  
Xiang Tian ◽  
Yao Wu Chen
Keyword(s):  
Statistical Learning ◽  
High Efficiency ◽  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Unit Size ◽  
High Efficiency Video Coding ◽  
Block Partitioning ◽  
Decision Algorithm ◽  
Intra Coding ◽  
Coding Unit

High Efficiency Video Coding (HEVC) is an ongoing standard, and it employs the quad-tree block partitioning structure which includes coding unit, prediction unit, and transform unit. This content-adaptive coding tree structure can improve HEVC coding efficiency significantly, but it also consumes large computational complexity. This paper proposed a fast intra coding unit size decision algorithm to reduce the heavy complexity of HEVC encoding. First, the proposed algorithm reduced unit sizes search by using the classifier, which is based on the statistical learning. Second, an early largest unit size decision was designed to skip the checking of unnecessary unit sizes. As compared to the full search algorithm in HEVC reference software, experimental results show that the proposed algorithm achieves 50.4% computation saving on average with 1.83% bit rate increase and 0.070dB peak signal-to-noise ratio loss.

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