Coding Unit Depth Level Research Articles

Efficient intra mode decision for low complexity HEVC screen content compression.

High efficiency video coding screen content coding (HEVC-SCC) extension is the latest HEVC development to improve the compression performance of screen content (SC) video. Similar to HEVC, the intra mode selection in HEVC-SCC is performed all the coding unit (CU) partitions to find the least rate distortion (RD) cost. Furthermore, additional intra tools are introduced to improve HEVC-SCC coding efficiency. However, these new tools could cause high computation complexity which restricts HEVC-SCC from ongoing applications. To solve the problem, an efficient intra mode decision for HEVC-SCC that adaptively utilizes the texture complexity of SC treeblock is proposed. The texture complexity of a SC treeblock is first analyzed according to the variation degree of the luminance value. And then, two efficient approaches are proposed based on the constructed model, which are early CU depth level determination and adaptive intra mode selection. Experimental results demonstrate that the proposed method can save 48.5% encoder runtime while keeping nearly the same coding efficiency as the HEVC-SCC encoders.

Low-Complexity Scalable Extension of the High-Efficiency Video Coding (SHVC) Encoding System

The scalable extension of the high-efficiency video coding (SHVC) system adopts a hierarchical quadtree-based coding unit (CU) that is suitable for various texture and motion properties of videos. Currently, the test model of SHVC identifies the optimal CU size by performing an exhaustive quadtree depth-level search, which achieves a high compression efficiency at a heavy cost in terms of the computational complexity. However, many interactive multimedia applications, such as remote monitoring and video surveillance, which are sensitive to time delays, have insufficient computational power for coding high-definition (HD) and ultra-high-definition (UHD) videos. Therefore, it is important, yet challenging, to optimize the SHVC coding procedure and accelerate video coding. In this article, we propose a fast CU quadtree depth-level decision algorithm for inter-frames on enhancement layers that is based on an analysis of inter-layer, spatial, and temporal correlations. When motion/texture properties of coding regions can be identified early, a fast algorithm can be designed for adapting CU depth-level decision procedures to video contents and avoiding unnecessary computations during CU depth-level traversal. The proposed algorithm determines the motion activity level at the treeblock size of the hierarchical quadtree by utilizing motion vectors from its corresponding blocks at the base layer. Based on the motion activity level, neighboring encoded CUs that have larger correlations are preferentially selected to predict the optimal depth level of the current treeblock. Finally, two parameters, namely, the motion activity level and the predicted CU depth level, are used to identify a subset of candidate CU depth levels and adaptively optimize CU depth-level decision processes. The experimental results demonstrate that the proposed scheme can run approximately three times faster than the most recent SHVC reference software, with a negligible loss of compression efficiency. The proposed scheme is efficient for all types of scalable video sequences under various coding conditions and outperforms state-of-the-art fast SHVC and HEVC algorithms. Our scheme is a suitable candidate for interactive HD/UHD video applications that are expected to operate in real-time and power-constrained scenarios.

Low-Complexity Texture Video Coding Based on Motion Homogeneity for 3D-HEVC

Three-dimensional extension of the high efficiency video coding (3D-HEVC) is an emerging international video compression standard for multiview video system applications. Similar to HEVC, a computationally expensive mode decision is performed using all depth levels and prediction modes to select the least rate-distortion (RD) cost for each coding unit (CU). In addition, new tools and intercomponent prediction techniques have been introduced to 3D-HEVC for improving the compression efficiency of the multiview texture videos. These techniques, despite achieving the highest texture video coding efficiency, involve extremely high-complex procedures, thus limiting 3D-HEVC encoders in practical applications. In this paper, a fast texture video coding method based on motion homogeneity is proposed to reduce 3D-HEVC computational complexity. Because the multiview texture videos instantly represent the same scene at the same time (considering that the optimal CU depth level and prediction modes are highly multiview content dependent), it is not efficient to use all depth levels and prediction modes in 3D-HEVC. The motion homogeneity model of a CU is first studied according to the motion vectors and prediction modes from the corresponding CUs. Based on this model, we present three efficient texture video coding approaches, such as the fast depth level range determination, early SKIP/Merge mode decision, and adaptive motion search range adjustment. Experimental results demonstrate that the proposed overall method can save 56.6% encoding time with only trivial coding efficiency degradation.

Efficient Intra Mode Selection for Depth-Map Coding Utilizing Spatiotemporal, Inter-Component and Inter-View Correlations in 3D-HEVC.

3D-high efficiency video coding (HEVC) is developed for the compression of the multi-view video plus depth format, which is based on the latest generation of video coding standard, HEVC. It further adopts several new intra prediction modes, depth-modeling modes (DMMs) in intra candidate modes for a better representation of edges in depth maps, which introduces a drastic increase in the computational complexity. The procedure of depth intra mode decision together with DMMs and existing intra modes is a very time consuming part due to huge complexity of full rate distortion (RD) cost calculation. In this paper, a low complexity intra mode selection algorithm is proposed to reduce complexity of depth intra prediction in both intra-frames and inter-frames. An experimental analysis is first performed to study the inter-view correlation and the inter-component (texture video and its associated depth) correlation in intra coding information such as the intra mode and RD cost. All intra modes available in 3D-HEVC are classified into three activity classes assigned with different mode-weight factors, and the coding mode complexity of a coding unit (CU) is defined according to the intra mode information from available spatiotemporal, inter-view, and inter-component neighboring coded CUs. The coding mode complexity analysis is utilized to assign different candidate intra modes for different types of CUs. The optimal intra prediction mode and the RD cost value in current CU depth level are further used to skip unnecessary intra prediction sizes. Experimental results show that the proposed fast depth intra coding algorithm achieves 61% complexity reduction on intra prediction, while incurring a 0.2% Bjontegaard metric increase for coded and synthesized views compared to the test model of 3D-HEVC.

Fast CU size decision and PU mode decision algorithm for quality SHVC inter coding

As a scalable extension of the High Efficiency Video Coding (HEVC), the Scalable High Efficiency Video Coding (SHVC) encoder needs to encode multiple HEVC layers with Inter-layer predictions, which causes the significant increase in coding complexity. In this paper, we proposed a novel Inter prediction scheme to effectively reduce computational complexity in Quality SHVC. The new features of the proposed algorithm include: First, spatial and Inter-layer depth correlations are used to predict the most possible coding unit (CU) depth level candidates. Second, a statistical test method on the current CU depth level is introduced to examine whether the residual coefficients within its block present similar distribution to terminate depth selection early. Finally, during Inter prediction selection from 8 Prediction Unit (PU) sizes, spatial and Inter-layer correlations are combined with residual coefficients distribution to determine the PU partitioning mode is Symmetric Motion Partitioning (SMP) or Asymmetric Motion Partitioning (AMP). Experimental results demonstrate that the proposed algorithm can save an average of 65.33% coding time of enhancement layer (EL) while achieving a better rate distortion (RD) performance over other state-of-the-art work.

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.

Fast intra coding based on CU size decision and direction mode decision for HEVC

High Efficiency Video Coding (HEVC) adopts a quad-tree structured coding unit (CU) and more prediction unit (PU) modes are used in all CU depth levels. These improvements bring better coding performance, but the exhaustive search process for optimal CU and PU selection causes higher computational complexity than earlier standards. To speed up the encoder, this paper proposes a fast intra coding algorithm. Firstly, a fast CU size decision method is introduced, which selects different depth decision methods for each largest coding unit. Secondly, a fast direction mode decision method is proposed. It first compares the direction modes of the parent unit and most probable modes (MPMs) list, and then the first direction mode of rate-distortion optimization (RDO) list is used to early terminate the RDO process. Experimental results show that compared with HM 10.0, the proposed algorithm achieves 47% time reduction, whereas the value of BDBR is only 0.7%.

Fast coding algorithm for HEVC based on video contents

High efficiency video coding (HEVC) achieves higher coding efficiency than previous standards but introduces a large computational complexity. Because HEVC adopted some new advanced tools and the most prominent one is the flexible hierarchical coding structures which include coding unit (CU), prediction unit (PU), transform unit (TU). All of those units must be test through rate-distortion optimisation. Since the CU is highly content dependent it is not efficient to test all the modes. In this study, the authors propose a fast coding algorithm for HEVC based on video contents. The authors statistically analysis the features of video contents from three aspects, the pixel gradient, the block mean value, and the block variance of CU. Then, jointly use these features with the CU depth levels and the prediction modes of spatiotemporal adjacent CUs to realise fast CU depth level decision and fast prediction mode decision. The experimental results show that the proposed algorithm can save 58.9 and 57.6% computational complexity on average with only average 1.8 and 1.9% bitrate losses under ‘random access, main’ and ‘low-delay, main’ conditions, respectively.

Context-adaptive based CU processing for 3D-HEVC.

The 3D High Efficiency Video Coding (3D-HEVC) standard aims to code 3D videos that usually contain multi-view texture videos and its corresponding depth information. It inherits the same quadtree prediction structure of HEVC to code both texture videos and depth maps. Each coding unit (CU) allows recursively splitting into four equal sub-CUs. At each CU depth level, it enables 10 types of inter modes and 35 types of intra modes in inter frames. Furthermore, the inter-view prediction tools are applied to each view in the test model of 3D-HEVC (HTM), which uses variable size disparity-compensated prediction to exploit inter-view correlation within neighbor views. It also exploits redundancies between a texture video and its associated depth using inter-component coding tools. These achieve the highest coding efficiency to code 3D videos but require a very high computational complexity. In this paper, we propose a context-adaptive based fast CU processing algorithm to jointly optimize the most complex components of HTM including CU depth level decision, mode decision, motion estimation (ME) and disparity estimation (DE) processes. It is based on the hypothesis that the optimal CU depth level, prediction mode and motion vector of a CU are correlated with those from spatiotemporal, inter-view and inter-component neighboring CUs. We analyze the video content based on coding information from neighboring CUs and early predict each CU into one of five categories i.e., DE-omitted CU, ME-DE-omitted CU, SPLIT CU, Non-SPLIT CU and normal CU, and then each type of CU adaptively adopts different processing strategies. Experimental results show that the proposed algorithm saves 70% encoder runtime on average with only a 0.1% BD-rate increase on coded views and 0.8% BD-rate increase on synthesized views. Our algorithm outperforms the state-of-the-art algorithms in terms of coding time saving or with better RD performance.

A fast inter-frame encoding scheme using the edge information and the spatiotemporal encoding parameters for HEVC

High Efficiency Video Coding (HEVC), as a novel video coding standard, has shown a better coding efficiency than all existing standards, such as H.264/AVC. It adopts a lot of new efficient coding tools, the most important one is the new hierarchical structures which include the coding unit (CU), prediction unit (PU) and transform unit (TU). However, the rate-distortion (RD) optimization process for all CUs, PUs, and TUs cause large computational costs. In this paper, a fast Inter-frame encoding scheme using the edge information and the spatiotemporal encoding parameters is proposed to reduce the encoder complexity of HEVC, which consists of a fast all 2 N × 2 N modes decision method, a fast CU depth level decision method and a fast PU mode decision method. This scheme uses edge information to express the structure complexity and uses the difference of edge information between current CU and its spatiotemporal CUs to express the edge similarity (ES) in one frame and the edge movement (EM) between two adjacent frames. And then, utilizes ES and EM as assistant parameters cooperate with CU depth levels and PU mode RD costs of spatiotemporal CUs to accomplish the early termination of CU split and the PU mode selection. The experimental results show that the proposed fast inter-frame encoding method can significantly reduce the computational costs with negligible RD loss. There are 53.7 and 54.9% encoding time savings on average, but only with average 1.5 and 1.8% Bjontegaard difference bitrate (BDBR) losses for various test sequences under random access and low delay conditions, respectively.

Efficient prediction of CU depth and PU mode for fast HEVC encoding using statistical analysis

High Efficiency Video Coding (HEVC) adopts complex quadtree-structured CU (coding unit) and PU (prediction unit) modes. Thus, the search process for optimal modes causes high computational complexity in HEVC. To solve this problem, a fast coding algorithm is proposed. Thirteen neighboring CTUs (Coding Tree Unit) are divided into three classes with the k-means method, and are selectively used as the reference CTUs to calculate the predicted CU depth levels for the current CTU. Additionally, for every CU depth, we skip some rarely used PU modes to reduce the complexity of the PU selection algorithm. Experimental results show that compared with the HEVC reference software, our algorithm can achieve 56.71% time saving with Low Delay Configuration profile and 59.76% with Random Access Configuration profile, whereas values of BDBR (Bjontegaard Delta Bit Rate) are only 1.0517% and 0.9918%, respectively. Compared with five state-of-the-art algorithms, the proposed algorithm has significant encoding time reduction with good bitrate performances.

Neyman-Pearson-Based Early Mode Decision for HEVC Encoding

The high efficiency video coding (HEVC) standard has highly improved the coding efficiency by adopting hierarchical structures of coding unit (CU), prediction unit (PU), and transform unit (TU). However, enormous computational complexity is introduced due to the recursive rate-distortion optimization (RDO) process on all CUs, PUs and TUs. In this paper, we propose a fast and efficient mode decision algorithm based on the Neyman-Pearson rule, which consists of early SKIP mode decision and fast CU size decision. First, the early mode decision is modeled as a binary classification problem of SKIP/non-SKIP or split/unsplit. The Neyman-Pearson-based rule is employed to balance the rate-distortion (RD) performance loss and the complexity reduction by minimizing the missed detection with a constrained incorrect decision rate. A nonparametric density estimation scheme is also developed to calculate the likelihood function of the statistical parameters. Furthermore, an online training scheme is employed to periodically update the probability density distributions for different quantization parameters (QPs) and CU depth levels. The experimental results show that the proposed overall algorithm can save 65% and 58% computational complexity on average with a 1.29% and 1.08% Bjontegaard Delta bitrate (BDBR) increase for various test sequences under random access and low delay P conditions, respectively. The proposed overall scheme also has the advantage that it can make the trade-off between the RD performance and time saving by setting different values for the incorrect decision rate.

Fast CU Splitting and Pruning for Suboptimal CU Partitioning in HEVC Intra Coding

High Efficiency Video Coding (HEVC), a new video coding standard currently being established, adopts a quadtree-based Coding Unit (CU) block partitioning structure that is flexible in adapting various texture characteristics of images. However, this causes a dramatic increase in computational complexity compared to previous video coding standards due to the necessity of finding the best CU partitions. In this paper, a fast CU splitting and pruning method is presented for HEVC intra coding, which allows for significant reduction in computational complexity with small degradations in rate-distortion (RD) performance. The proposed fast splitting and pruning method is performed in two complementary steps: 1) early CU split decision and 2) early CU pruning decision. For CU blocks, the early CU splitting and pruning tests are performed at each CU depth level according to a Bayes decision rule method based on low-complexity RD costs and full RD costs, respectively. The statistical parameters for the early CU split and pruning tests are periodically updated on the fly for each CU depth level to cope with varying signal characteristics. Experimental results show that our proposed fast CU splitting and pruning method reduces the computational complexity of the current HM to about 50% in encoding time with only 0.6% increases in BD rate.

Fast CU size decision and mode decision algorithm for HEVC intra coding

The emerging international standard of High Efficiency Video Coding (HEVC) is a successor to H.264/AVC. In the joint model of HEVC, the tree structured coding unit (CU) is adopted, which allows recursive splitting into four equally sized blocks. At each depth level, it enables up to 34 intra prediction modes. The intra mode decision process in HEVC is performed using all the possible depth levels and prediction modes to find the one with the least rate distortion (RD) cost using Lagrange multiplier. This achieves the highest coding efficiency but requires a very high computational complexity. In this paper, we propose a fast CU size decision and mode decision algorithm for HEVC intra coding. Since the optimal CU depth level is highly content-dependent, it is not efficient to use a fixed CU depth range for a whole image. Therefore, we can skip some specific depth levels rarely used in spatially nearby CUs. Meanwhile, there are RD cost and prediction mode correlations among different depth levels or spatially nearby CUs. By fully exploiting these correlations, we can skip some prediction modes which are rarely used in the parent CUs in the upper depth levels or spatially nearby CUs. Experimental results demonstrate that the proposed algorithm can save 21% computational complexity on average with negligible loss of coding efficiency.