Fast Mode Decision Algorithm Research Articles

Fast rough mode decision algorithm and hardware architecture design for AV1 encoder

Fast rough mode decision algorithm and hardware architecture design for AV1 encoder

A novel fast intra algorithm for VVC based on histogram of oriented gradient

The latest Versatile Video Coding (VVC) standard incorporates a series of effective and complex new intra coding tools, which obtains superior coding efficiency than the High Efficiency Video Coding (HEVC). However, this makes the intra coding more complicated and time-consuming. A fast algorithm for VVC from two aspects of fast mode decision and fast partition decision is proposed in this paper. For the fast mode decision, the relationship between bins with Histogram of Oriented Gradient (HOG) and intra modes is created for the mode selection, decreasing the planar modes for SATD and RDO. Moreover, we analyze the maximum bins to determine the final modes, and we use the modes of left and upper blocks as a reference for the current CU, which can early terminate RDO. Moreover, a two-step fast partition algorithm is proposed based on HOG for fast partition decision, in which two thresholds are investigated to control the uniformity of textures. The proposed fast algorithm is implemented on the VVC test model, and the experimental results show that it can achieve 69.07% time savings with only 2.96% BDBR increases averagely, which outperforms other relatively existing state-of-the-art methods. Moreover, to convince the universality of our algorithm, we further implement our method in Fraunhofer Versatile Video Encoder (VVenc) and Fraunhofer Versatile Video Decoder (VVdec), which have five settings to control the trade-off between encoding quality and efficiency for intra coding. The fast intra mode decision algorithm and fast partition algorithm decrease the complexity of intra coding for both VTM and VVenc, which shows the efficiency and universality of the proposed fast partition and fast mode decision algorithms.

A support vector machine based fast planar prediction mode decision algorithm for versatile video coding

A support vector machine based fast planar prediction mode decision algorithm for versatile video coding

Efficient mode decision scheme based on edge detection with Gaussian pulse for Intra-prediction in H.264/AVC

Advanced Video Coding (H.264/AVC) is one of the most important video coding standards which was developed by the ITU-T Video Coding Experts Group (VCEG) together with the ISO/IEC Moving Picture Experts Group (MPEG). Due to the complex procedures needed to find the optimal mode in intra prediction mode stage of H.264/AVC, an efficient mode decision is suggested in this paper. The main objectives of the proposed work are improving the compression efficiency of video coding and minimizing the degradation of video quality. The proposed algorithm is based on the gradient method for edge detection using the Prewitt filter to determine the texture direction of a block to be predicted in the luminance component of a video frame. To decide either implemented the DC mode or the edge-detection based gradient method to predict block under consideration, a predefined threshold value is used for the homogeneity test of each macro-block. To reduce the Bit Rate of the compressed video, Gaussian pulse modulation is also suggested to the Discrete Cosine Transformation (DCT) components of each macro-block during the coding process. The proposed algorithm is carried out on a luminance component of a set of different YUV video sequences with different resolutions and different quantization parameters using MATLAB. The suggested algorithm is evaluated using different metrics which are Peak Signal to Noise Ratio (PSNR) and bit rate measured by Bjontegaard Delta method. The simulation results show the ability of the proposed algorithm in enhancing the BDPSNR averagely by 1.14 dB and reducing the BDBR averagely by 11.34 %for QCIF sequences compared to the existing state-of-the-art fast intra-prediction mode decision algorithms.

CNN-LSTM Learning Approach-Based Complexity Reduction for High-Efficiency Video Coding Standard

High-Efficiency Video Coding provides a better compression ratio compared to earlier standard, H.264/Advanced Video Coding. In fact, HEVC saves 50% bit rate compared to H.264/AVC for the same subjective quality. This improvement is notably obtained through the hierarchical quadtree structured Coding Unit. However, the computational complexity significantly increases due to the full search Rate-Distortion Optimization, which allows reaching the optimal Coding Tree Unit partition. Despite the many speedup algorithms developed in the literature, the HEVC encoding complexity still remains a crucial problem in video coding field. Towards this goal, we propose in this paper a deep learning model-based fast mode decision algorithm for HEVC intermode. Firstly, we provide a deep insight overview of the proposed CNN-LSTM, which plays a kernel and pivotal role in this contribution, thus predicting the CU splitting and reducing the HEVC encoding complexity. Secondly, a large training and inference dataset for HEVC intercoding was investigated to train and test the proposed deep framework. Based on this framework, the temporal correlation of the CU partition for each video frame is solved by the LSTM network. Numerical results prove that the proposed CNN-LSTM scheme reduces the encoding complexity by 58.60% with an increase in the BD rate of 1.78% and a decrease in the BD-PSNR of -0.053 dB. Compared to the related works, the proposed scheme has achieved a best compromise between RD performance and complexity reduction, as proven by experimental results.

Optimised HEVC encoder intra‐only configuration

High-efficiency video coding (HEVC) is the latest video coding standard aimed to reduce the bitrate by half for the same video quality compared to H.264/AVC. This encoding performance makes HEVC more suitable for high-definition video applications. However, this performance is coupled with a high-computational complexity, which makes it hard to achieve real-time video encoding with a classic embedded processor. Multicore technology of programmable processors could be a very promising solution to overcome this computational complexity. Moreover, software optimisations by proposing fast algorithms for the most complex functions could also be an efficient solution to speed up the encoding process. In this context, this study presents a fast mode decision algorithm for the intra prediction module. This algorithm aims to reduce the number of intra prediction modes to be tested instead of performing a full intra mode search. Experimental results for all-Intra configuration show that the proposed fast intra mode decision allows saving up to 46.79% of the intra prediction time in average. Encoding performance in terms of video quality and bitrate is not significantly affected.

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

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.

An optimal mode selection algorithm for scalable video coding

Scalable video coding (SVC) is extended from its predecessor advanced video coding (AVC) because of its flexible transmission to all type of gadgets. However, SVC is more flexible and scalable than AVC, but it is more complex in determining the computations than AVC. The traditional full search method in the standard H.264 SVC consumes more encoding time for computation. This complexity in computation need to be reduced and many fast mode decision (FMD) algorithms were developed, but many fail to balance in all the three measures such as peak signal to noise ratio (PSNR), encoding time and bit rate. In this paper, the proposed optimal mode selection algorithm based on the orientation of pixels achieves better time saving, good PSNR and coding efficiency. The proposed algorithm is compared with the standard H.264 JSVM reference software and found to be 57.44% time saving, 0.43 dB increments in PSNR and 0.23% compression in bit rate.

An optimal mode selection algorithm for scalable video coding

Scalable video coding (SVC) is extended from its predecessor advanced video coding (AVC) because of its flexible transmission to all type of gadgets. However, SVC is more flexible and scalable than AVC, but it is more complex in determining the computations than AVC. The traditional full search method in the standard H.264 SVC consumes more encoding time for computation. This complexity in computation need to be reduced and many fast mode decision (FMD) algorithms were developed, but many fail to balance in all the three measures such as peak signal to noise ratio (PSNR), encoding time and bit rate. In this paper, the proposed optimal mode selection algorithm based on the orientation of pixels achieves better time saving, good PSNR and coding efficiency. The proposed algorithm is compared with the standard H.264 JSVM reference software and found to be 57.44% time saving, 0.43 dB increments in PSNR and 0.23% compression in bit rate.

Fast CU size and mode decision algorithm for 3D-HEVC intercoding

The 3D High Efficiency Video Coding (3D-HEVC) is an extension of the last 2D video coding generation (HEVC) using the Multi-view Video plus Depth map (MVD) format. In inter-coding process, the 3D-HEVC uses multiples inter prediction modes for each Coding Unit (CU) size. These prediction modes are performed to use all the possible CU sizes to select the one with the least Rate Distortion (RD) cost as the best prediction mode using Lagrange multiplier. It achieved high coding efficiency but it consumes an extremely time coding which limits 3D-HEVC from real-time applications. In this paper, a fast inter prediction mode and CU size decision algorithm is proposed to reduce the complexity of 3D-HEVC inter-coding. This algorithm is based on the homogeneity of CU using structure tensor. An experimental analysis is first performed to study the correlation of the CU complexity with depth levels and prediction modes, then the structure tensor of each CU is extracted to characterize the CU homogeneity, which has strong relationship with coding levels and inter prediction modes. Finally, a fast algorithm is adopted to accomplish an early termination of CU split and skip unnecessary prediction modes. The experimental results demonstrate that the proposed fast algorithm can provide a considerable encoding time-saving with negligible rate-distortion performance loss compared to the original 3D-HEVC encoder.

Adaptive inter CU partitioning based on a look-ahead stage for HEVC

High Efficiency Video Coding (HEVC) has become the state-of-the-art video coding standard. It outperforms its predecessors by the introduction of new coding tools, such as the new quadtree-based partitioning scheme called the coding tree unit (CTU), which enables a more flexible partitioning of the input frames. However, selecting the optimal partitioning requires the evaluation of numerous possibilities, which involves long computing times that hinder the applicability of the standard in real-world scenarios. With this in mind, the main focus of this paper is on tackling this complexity by means of a fast partitioning and mode decision algorithm based on a look-ahead stage. This stage performs a preliminary motion estimation that provides the motion costs used to build the least-cost quadtree, which is in turn utilized to conduct the encoding itself. On the basis of this quadtree, the encoder may decide to terminate the partitioning early, or to evaluate additional depth levels adaptively. Furthermore, the encoder may omit some prediction modes according to the costs estimated by the look-ahead stage. A thorough experimental evaluation of the algorithm shows that it can reduce the encoding time by 65.33%, at the expense of only a 1.35% BD-rate for the random access configuration. Combined with a fast inter prediction algorithm, this reduction can rise to 70.55%, while the coding efficiency is maintained at a 1.83% BD-rate. When compared with other related works, these results display an excellent trade-off between the two variables.

A Fast Inter Mode Decision Algorithm for HEVC Encode System based on Spatial and Temporal Correlations

A Fast Inter Mode Decision Algorithm for HEVC Encode System based on Spatial and Temporal Correlations

Fast Mode Decision Based on Gradient Information in 3D-HEVC

The 3D extension of High Efficiency Video Coding (3D-HEVC) introduce Depth Modeling Mode (DMM) and 35 conventional intra modes to enhance the quality of coding, while bringing unacceptable computational complexity during the process of rough mode decision (RMD) and most probable mode (MPM). In this paper, we proposed a fast mode decision algorithm for texture map and depth map coding based on gradient information. Firstly, analyzing the characteristics of predict units (PU) in different intra mode applications that obtain the lowest RD-cost,then extracting the gradient information of PU to classify the PU into three types of gradient blocks and selecting appropriate candidate modes for the PU,thereby avoiding search each mode in coding process and skip the process of RMD and MPM early. Experimental results show that the proposed algorithm can achieve average 30.6% time saving with negligible reduction of coding performance.

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.

Machine Learning-Based Fast Angular Prediction Mode Decision Technique in Video Coding.

In this paper we propose a machine learning-based fast intra-prediction mode decision algorithm, using random forest that is an ensemble model of randomized decision trees. The random forest is used to estimate an intra-prediction mode from a prediction unit and to reduce encoding time significantly by avoiding the intensive Rate-Distortion optimization of a number of intra-prediction modes. To this aim, we develop a randomized tree model including parameterized split functions at nodes to learn directional block-based features. The feature uses only four pixels reflecting a directional property of a block, and, thus the evaluation is fast and efficient. To integrate the proposed technique into the conventional video coding standard frameworks, the intra-prediction mode derived from the proposed technique, called an inferred mode (IM), is used to shrink the pool of the candidate modes before carrying out the Rate-Distortion (R-D) optimization. The proposed technique is implemented into the High Efficiency Video Coding Test Model (HM) reference software of the state-of-the-art video coding standard and Joint Exploration Model (JEM) reference software, by integrating the random forest trained off-line into the codecs. Experimental results demonstrate that the proposed technique achieves significant encoding time reduction with only slight coding loss as compared the reference software models.

A fast CU partition and mode decision algorithm for HEVC intra coding

The latest video coding standard H.265/HEVC is a milestone because it can achieve up to 50 % coding performance improvement compared to the widely used H.264/AVC, owing to the newly introduced coding tools. However, these new techniques also introduce dramatic computational complexity increase. In order to release the computational burden while maintaining the coding efficiency, a two-part fast intra coding algorithm is proposed in this paper. First a CTU depth range prediction and two Bayesian decision rules are applied to accelerate the CU partition process. The depth search range of the current CTU is restricted by the neighboring CTUs on account of the spatial correlation. And one early split and one early termination strategies is presented based on the minimum Bayesian decision rule. Next, a three-step fast intra mode decision algorithm is performed to speed up the intra coding, both the depth information and the orientation information of the current CU are used to skip some unnecessary intra mode check, and the order of Most Probable Modes (MPM) is adjusted to further remove redundant calculation. As demonstrated in the experimental results, our proposed algorithm can reduce 52.9% encoding time on average with the cost of an negligible coding efficiency loss under the All-Intra configuration.

Low complexity intra prediction mode decision for 3D-HEVC depth coding

The 3D High Efficiency Video Coding (3D-HEVC) is the latest 3D extension of the HEVC video coding standard. It supports multi-view videos plus depth (MVD), which is a sophisticated format for enhanced 3D content. Depth modeling modes (DMM) are adopted in the 3D-HEVC for better sharp edge encoding. However, employing DMM causes important increase of computational complexity. In this paper, we propose a three steps scheme for fast intra depth coding in 3D-HEVC. Based on the kirsch edge detection algorithm, a fast angular mode decision algorithm is performed. In order to avoid unnecessary evaluation of the DMMs, a new early termination approach (ET) based on the smoothness of the processed block is adopted. Besides, based on the enhanced shuffled frog leaping algorithm (E-SFLA), a new heuristic method to search the optimal wedgelet pattern for DMM1 Depth mode is introduced. The proposed algorithm is implemented on the top of four different versions of HTM. It outperforms HTM-16.0, the latest used version, and four different work from the literature, in terms of encoding time with similar coding and quality efficiency.

A Fast Mode Decision Algorithm Using Hierarchical and Skip Methods for Intra Prediction in HEVC

This paper proposes an adaptive fast mode decision (AFMD) algorithm for intra prediction in high efficiency video coding (HEVC) using hierarchical and skip methods. AFMD consists of two phases: an adaptive hierarchical mode decision and an adaptive rate-distortion optimization. The proposed AFMD focuses mainly on reducing the number of prediction modes, and thus it can decrease computation time. For rough mode decision and rate-distortion optimization, AFMD computes candidate modes in a way that includes only those intra modes that are most promising. In our experiments, AFMD shows times saving of 41.5% and 40.58% compared to the HEVC test model (HM) reference software, versions 14.0 and 16.0. In addition, the proposed algorithm increases the Bjontegaard delta bitrate by only about 1.00% and 0.93% compared to HM 14.0 and HM 16.0.

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