Fast CU Size Decision Research Articles

Fast CU size decision and intra-prediction mode decision method for H.266/VVC

AbstractH.266/Versatile Video Coding (VVC) is the most recent video coding standard developed by the Joint Video Experts Team (JVET). The quad-tree with nested multi-type tree (QTMT) architecture that improves the compression performance of H.266/VVC is introduced. Moreover, H.266/VVC contains a greater number of intra-prediction modes than H.265/High Efficiency Video Coding (HEVC), totalling 67. However, these lead to extremely the coding computational complexity. To cope with the above issues, a fast intra-coding unit (CU) size decision method and a fast intra-prediction mode decision method are proposed in this paper. Specifically, the trained Support Vector Machine (SVM) classifier models are utilized for determining CU partition mode in a fast CU size decision scheme. Furthermore, the quantity of intra-prediction modes added to the RDO mode set decreases in a fast intra-prediction mode decision scheme based on the improved search step. Simulation results illustrate that the proposed overall algorithm can decrease 55.24% encoding runtime with negligible BDBR.

Unsupervised learning-based fast CU size decision for geometry videos in V-PCC

Unsupervised learning-based fast CU size decision for geometry videos in V-PCC

Fast CU size decision algorithm for VVC intra coding

Fast CU size decision algorithm for VVC intra coding

A Fast CU Size Decision Optimal Algorithm Based on Neighborhood Prediction for HEVC

A Fast CU Size Decision Optimal Algorithm Based on Neighborhood Prediction for HEVC

Fast CU size decision based on texture-depth relationship for depth map encoding in 3D-HEVC

Since many advanced encoding techniques are introduced into the 3D-HEVC, it achieves higher encoding efficiency compared to HEVC. However, the encoding complexity of 3D-HEVC increases significantly when these high complexity coding techniques are used. In this paper, a new fast CU size decision algorithm based on texture-depth is proposed to reduce the depth map encoding complexity. Since there is strong correlation between texture and depth map, including motion characteristic and background region, both kinds of maps tend to choose the same CU depth as their best depth level. By building one-to-one match for collocated largest coding unit (LCU), the information of texture encoding can be used to predict the depth level of depth map. Experimental results have shown that the proposed method can achieve 41.89% time saving on average, with the negligible drop of 0.04 dB on BDPSNR and a small increase of 2.29% on BDBR.

Fast CU size decision based on texture-depth relationship for depth map encoding in 3D-HEVC

Fast CU size decision based on texture-depth relationship for depth map encoding in 3D-HEVC

Fast intra mode decision and fast CU size decision for depth video coding in 3D-HEVC

3D-HEVC is the new coding standard for both texture and associated depth video. In addition to several new coding tools that are designed for texture video with improved coding efficiency, some tools are specified for depth video, such as depth modeling modes (DMMs), segment-wise DC (SDC) mode and depth intra skip mode. This paper proposes techniques that accelerate the encoding of depth video, including a fast intra mode decision and a fast CU size decision. A hierarchical mode decision is proposed for the fast intra mode decision, based on a statistical analysis of mode distribution. The intra mode can be predicted at a very early stage if the hierarchical comparison of the rate–distortion (R–D) cost for selected candidate modes is satisfied for a given condition. For the fast CU size decision, smaller CU sizes are not evaluated if the current CU has the desired properties. The CU size of the collocated texture is also considered to avoid over-splitting of the current depth CU. The experimental results show that the proposed techniques achieve an average 62.5% time saving with 0.81% increase in the bitrate for the synthesized views under the all intra configuration, compared with the reference software HTM 15.0.

CART-based fast CU size decision and mode decision algorithm for 3D-HEVC

Despite three-dimensional high efficiency video coding (3D-HEVC) has a good performance of 3D video coding and synthesized views, the recursive splitting process of the largest coding unit (LCU) and the best mode deciding process caused huge computational complexity. To reduce this computational burden, this paper presents a classification and regression tree-based (CART) fast coding level decision and mode decision algorithm for 3D depth video. The algorithm contained two parts: CART model training, fast coding level and mode decision. In the part of CART model training, we constructed a CART decision tree, where the complexity of depth map, the optimal depth level of co-located texture and the relativity of neighbor LCU were regarded as feature vectors, and the best coding unit depth level was regarded as class label. In the part of fast coding process, features were extracted to predict the depth level of each LCU of depth map; furthermore, some decision of coding mode could be skipped early. Experimental results show that proposed algorithm can save the times of coding process by 24.6% on average while maintaining almost the same rate distortion performance as the 3D-HEVC reference software.

Bayesian-theory-based Fast CU Size and Mode Decision Algorithm for 3D-HEVC Depth Video Inter-coding

Bayesian-theory-based Fast CU Size and Mode Decision Algorithm for 3D-HEVC Depth Video Inter-coding

Fast CU Size Decision for HEVC Intra Coding by Using Local Characteristics and RD Costs

Fast CU Size Decision for HEVC Intra Coding by Using Local Characteristics and RD Costs

Fast and effective CU size decision based on spatial and temporal homogeneity detection

High-Efficiency Video Coding (HEVC) is the latest video coding standard of the Joint Collaborative Team on Video Coding (JCT-VC). HEVC noticeably improves compression performance when compared with previous standards such as H264, and represents a major step forward in video compression technology. However, this improvement is achieved by increasing the complexity of the encoding process. HEVC employs a novel flexible quad-tree coding block partitioning structure that enables the use of large and multi-sized coding, prediction, and transform blocks. This system is more efficient but also more computationally demanding. In this article an optimized CU size decision algorithm is proposed to reduce the computational cost of quad-tree partitioning by means of spatial and temporal homogeneity analysis and classification, which are directly applied to the input image. If a CU is classified as spatially or temporally homogeneous the quad-tree recursive process is stopped. Furthermore, this image pre-analysis is performed using logic units and embedded hardware on a GPU, thus avoiding unnecessary waiting states, so the computational cost associated with this process is zero for the processor in charge of the encoding process. In comparison with the reference HM16.2 test model, the encoding time is reduced by up to 32.69%, with negligible quality loss and a maximum BD-Rate increase of 1.2% for low-delay P configuration.

HEVC 인트라 코딩을 위한 모멘트 기반 고속 CU크기 결정 방법

HEVC 비디오 압축 표준은 기존 비디오 표준보다 더 다양한 블록 구조와 예측 모드를 사용함으로써 우수한 부호화 성능을 제공하나, 최적의 블록 크기 및 예측 모드를 결정하기 위한 RDO(Rate Distortion Optimization)과정으로 인해 연산량이 많다는 단점을 가진다. 이를 개선하기 위해 본 논문에서는 화면 내 예측 수행 전 CU영역의 모멘트 값을 계산하고 이를 CU영역의 텍스쳐 복잡도로 이용하여 CU의 분할 여부를 결정하는 모멘트 기반의 고속 CU크기 결정 방법을 제안한다. 제안하는 방법은 기존의 방법을 차용하여 CU영역의 밝기 값에 대한 분산 값을 계산하여 영역의 텍스쳐 평평도를 추정하고, 추가로 CU영역의 밝기 값에 대한 비대칭도를 계산하여 CU영역을 이루는 밝기 값 분포의 비대칭성 정도를 측정한 뒤 이를 조합하여 기존 방법보다 더 정밀하게 텍스쳐 복잡도를 측정하였으며, 이를 RDO과정 중 현재 CU의 분할 여부를 결정하는데 이용하여 기존의 부정확한 CU분할 여부 결정 방법을 개선시킨 고속 CU크기 결정 방법을 제안한다. 제안 방법의 실험 결과는 기존 방법 대비 4.2%의 BD-rate 감소를 보여주며, HM-10.0과 비교하여 BD-rate는 1.1% 증가하였고, 인코딩 시간이 32% 절감되었다. The High Efficiency Video Coding (HEVC) standard provides superior coding efficiency by utilizing highly flexible block structure and more diverse coding modes. However, rate-distortion optimization (RDO) process for the decision of optimal block size and prediction mode requires excessive computational complexity. To alleviate the computation load, this paper proposes a new moment-based fast CU size decision algorithm for intra coding in HEVC. In the proposed method, moment values are computed in each CU block to estimate the texture complexity of the block from which the decision on an additional CU splitting procedure is performed. Unlike conventional methods which are mostly variance-based approaches, the proposed method incorporates the third-order moments of the CU block in the design of the fast CU size decision algorithm, which enables an elaborate classification of CU types and thus improves the RD-performance of the fast algorithm. Experimental results show that the proposed method saves 32% encoding time with 1.1% increase of BD-rate compared to HM-10.0, and 4.2% decrease of BD-rate compared to the conventional variance-based fast algorithm.