Fast Mode Decision Scheme 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.

Hybrid linear weighted prediction and intra block copy based light field image coding

Light field imaging can capture both spatial and angular information of a 3D scene and is considered as a prospective acquisition and display solution to supply a more natural and fatigue-free 3D visualization. However, one problem that occupies an important position to deal with the light field data is the sheer size of data volume. In this context, efficient coding schemes for this particular type of image are needed. In this paper, we propose a hybrid linear weighted prediction and intra block copy based light field image codec architecture based on high efficiency video coding screen content coding extensions (HEVC SCC) standard to effectively compress the light field image data. In order to improve the prediction accuracy, a linear weighted prediction method is integrated into HEVC SCC standard, where a locally correction weighted based method is used to derive the weight coefficient vector. However, for the non-homogenous texture area, a best match in linear weighted prediction method does not necessarily lead to a good prediction of the coding block. In order to alleviate such shortcoming, the proposed hybrid codec architecture explores the idea of using the intra block copy scheme to find the best prediction of the coding block based on rate-distortion optimization. For the reason that the used “try all then select best” intra mode decision method is time-consuming, we further propose a fast mode decision scheme for the hybrid codec architecture to reduce the computation complexity. Experimental results demonstrate the advantage of the proposed hybrid codec architecture in terms of different quality metrics as well as the visual quality of views rendered from decompressed light field content, compared to the HEVC intra-prediction method and several other prediction methods in this field.

Fast mode decision based on systematic deployment of predictive resources for H.264/AVC

This paper presents a well organised fast mode decision scheme for H.264/AVC that systematically integrates and deploys applicable predictive resources using constructive compromise. This scheme is constructed by primary SKIP mode checking, spatial prior temporal hard decision (STHD), and succinct mode decision (SMD). The STHD is connected with the predictive macroblocks searched. In STHD, only when spatial hard decision fails, temporal hard decision is performed using the potential partition characteristic vector (PPCV) addressed. If it fails, too, then SMD takes care of mode checking also using PPCV. The SMD consists of succinct P16 × 16 mode decision and succinct geometric/statistical extension examination, which are formed as an effective two-stage judgment. Extensive experimental results demonstrate that the proposed method by systematically deploying the predictive resources can achieve 58–74% runtime savings in encoding QCIF, CIF and high definition videos with low performance losses. In addition, most processes of the proposed scheme could be implemented on logical operations, only.

Fast mode decision scheme using sum of the absolute difference‐based Bayesian model for the H.264/AVC video standard

H.264/AVC is the most widely used recent video coding standard. It provides a high encoding efficiency but it also has a high computational complexity. The block mode decision for motion estimation is the most time-consuming procedure. A complexity reduction method for the block mode decision procedure is proposed. To reduce the complexity, all block modes are divided into several candidate block mode groups. The sum of the absolute difference (SAD) value, including the motion cost of each mode, is used as a classification feature to divide the block modes into several groups. A refinement method using a Bayesian model based on the average SAD value is also proposed. For B-slices, a differential block mode allocation method is suggested. A different number of candidate modes are allocated for lists (list 0, list 1) based on the SAD value of each list after 16 × 16 block motion estimation. The proposed method achieves an average time-saving for the total encoding time of 65% for IPPP and 66.01% for the hierarchical-B structure.

Fast macroblock mode decision for H.264/AVC baseline profile video transcoder based on support vector machines

In video transcoding, accuracy and efficiency of macroblock mode decision are critical issues at the re-encoder side due to the changes in frame size, frame rate, and bit rate. In this paper, a fast macroblock mode decision scheme based on support vector machines is proposed for H.264/AVC baseline profile video transcoder. Features including motion vectors, residual data, pre-encoded macroblock modes, and quantization parameters are extracted from incoming bitstream in both of training stage and classification stage. Feature extraction methods are investigated for spatial resolution transcoder, temporal resolution transcoder, and bit-rate transcoder. After off-line training and simplification of support vectors, the obtained support vector machine classifier can determine macroblock mode in the re-encoder accurately. Extensive experiments are carried out on different types of transcoders and results show that the proposed method can save about 80% in computational complexity compared to full mode search algorithm implemented in the latest H.264/AVC reference software (JM17.1), while maximum peak signal-to-noise ratio is degraded by 0.2–1.1 dB depending on different sequences and bit rate.

Fast intermode decision algorithm based on contextual mode and priority information for H.264/AVC video encoding system

The recent H.264/AVC video coding standard provides a higher coding efficiency than previous standards. H.264/AVC achieves a bit rate saving of more than 50% with many new technologies, but it shows very heavy computational complexity. In this paper, a fast mode decision scheme for interframe coding is proposed to reduce the computational complexity for H.264/AVC video encoding system. To reduce the block mode decision complexity in interframe coding, we use the contextual information based on the co-located and neighboring macroblocks (MBs) to detect a proper MB that can be stopped early. Then, for the current MB, a priority information of the context is suggested for adding more mode types adaptively. The proposed algorithm shows the average speed-up factors of 59.11 to 77.41% for various sequences with a negligible bit increment and a minimal loss of image quality, in JM reference softwares.

H.264-based depth map-sequence coding algorithm using mode mapping for fast 3-D video compression

An H.264-based fast 3-D video compression algorithm is pre- sented by reducing coding complexity of P slices in a depth map sequence using a fast-mode decision scheme. On the basis of the relationship of coding modes of collocated macroblocks on texture and depth frames, an early SKIP-mode decision scheme and a selective prediction-mode decision scheme have been developed to rapidly encode depth map se- quences. Experimental results show that the algorithm can save the entire encoding time by 79% on average compared to conventional a depth map sequence coding algorithm, while introducing only negligible loss in peak signal-to-noise ratio value. C 2011 Society of Photo-Optical Instrumentation Engineers

Fast mode decision algorithm for H.264 using statistics of rate-distortion cost

A context-based fast mode decision scheme for P slices in the H.264 video coding standard is presented. Based on the coding modes and the statistical distribution of rate-distortion costs of the collocated macroblocks in the previous frames, a context modelling technique has been developed to design adaptive threshold values for the fast mode decision. The simulation results show that the proposed method can reduce the encoding time by about 83% on average without significant loss of rate-distortion performance.

Fast Mode Prediction for the Baseline and Main Profiles in the H.264 Video Coding Standard

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We propose a novel fast mode decision scheme for both the baseline and main profiles of the H.264 video coding standard. Our scheme combines a set of conditions for SKIP mode decision, two heuristics that reduce the set of intermodes to be examined, inter/intramode prediction and the monotonicity property of the cost functions. For very similar RD performance, we achieve (33%–90%) reduction in run times as compared to H.264. Compared to other work that was used as input to the standard, our scheme is faster by 9–23% for very similar RD performance. The proposed scheme can be used either in its entirety or as individual components for speeding up the standard. It can be also used in conjunction with fast motion search methods and is applicable in both the rate controlled and non rate controlled cases of H.264. </para>

Fast Mode Decision Algorithm for Inter-Frame Coding in Fully Scalable Video Coding

Scalable video coding is an ongoing standard, and the current working draft (WD) is an extension of H.264/AVC. In the WD, an exhaustive search technique is employed to select the best coding mode for each macroblock. This technique achieves the highest possible coding efficiency, but it results in extremely large encoding time which obstructs it from practical use. This paper proposes a fast mode decision algorithm for inter-frame coding for spatial, coarse grain signal-to-noise ratio, and temporal scalability. It makes use of the mode-distribution correlation between the base layer and enhancement layers. Specifically, after the exhaustive search technique is performed at the base layer, the candidate modes for enhancement layers can be reduced to a small number based on the correlation. Experimental results show that the fast mode decision scheme reduces the computational complexity significantly with negligible coding loss and bit-rate increases

Fast mode decision algorithm for intraprediction in H.264/AVC video coding

The H.264/AVC video coding standard aims to enable significantly improved compression performance compared to all existing video coding standards. In order to achieve this, a robust rate-distortion optimization (RDO) technique is employed to select the best coding mode and reference frame for each macroblock. As a result, the complexity and computation load increase drastically. This paper presents a fast mode decision algorithm for H.264/AVC intraprediction based on local edge information. Prior to intraprediction, an edge map is created and a local edge direction histogram is then established for each subblock. Based on the distribution of the edge direction histogram, only a small part of intraprediction modes are chosen for RDO calculation. Experimental results show that the fast intraprediction mode decision scheme increases the speed of intracoding significantly with negligible loss of peak signal-to-noise ratio.