Intra Prediction In High Efficiency Video Coding Research Articles

High Performance VLSI Architecture for DC and Planar Modes of Intra Prediction in HEVC

Abstract: High Efficiency Video Coding (HEVC) achieves improved compression efficiency, but it introduces higher computational complexity due to intricate partitioning and increased angular modes in intra prediction. In this study, we propose a hardware architecture for handling the direct current (DC) and planar modes of intra prediction in the HEVC standard. To evaluate our proposed architecture, we performed synthesis using both TSMC 180nm and TSMC 90nm technologies. We later conducted physical implementation using Physical Cell 90nm technology. The results show significant improvements when moving from TSMC 180nm to TSMC 90nm technology, with the chip area reducing by approximately three times and power consumption decreasing by over six times. After completing the physical layout phase, we obtained a chip area of 112.19 mm2 and a power consumption of 3.88 mW. Comparing the synthesis results with the physical design phase, we observed a slight increase in chip area by around 1.5 times, while the power consumption decreased by approximately 0.4 times. The proposed architecture achieves a throughput of 27 pixels per clock cycle and supports a block size of 16x16, with the potential for further extension

Fast Decision Algorithm of CU Size for HEVC Intra-Prediction Based on a Kernel Fuzzy SVM Classifier

High Efficiency Video Coding (HEVC) achieves a significant improvement in compression efficiency at the cost of extremely high computational complexity. Therefore, large-scale and wide deployment applications, especially mobile real-time video applications under low-latency and power-constrained conditions, are more challenging. In order to solve the above problems, a fast decision method for intra-coding unit size based on a new fuzzy support vector machine classifier is proposed in this paper. The relationship between the depth levels of coding units is accurately expressed by defining the cost evaluation criteria of texture and non-texture rate-distortion cost. The fuzzy support vector machine is improved by using the information entropy measure to solve the negative impact of data noise and the outliers problem. The proposed method includes three stages: the optimal coded depth level “0” early decision, coding unit depth early skip, and optimal coding unit early terminate. In order to further improve the rate-distortion complexity optimization performance, more feature vectors are introduced, including features such as space complexity, the relationship between coding unit depths, and rate-distortion cost. The experimental results showed that, compared with the HEVC reference test model HM16.5, the proposed algorithm can reduce the encoding time of various test video sequences by more than 53.24% on average, while the Bjontegaard Delta Bit Rate (BDBR) only increases by 0.82%. In addition, the proposed algorithm is better than the existing algorithms in terms of comprehensively reducing the computational complexity and maintaining the rate-distortion performance.

Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction

The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjøntegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches.

An Adaptive CU Size Decision Algorithm for HEVC Intra Prediction Based on Complexity Classification Using Machine Learning

High efficiency video coding (HEVC), which is the newest video coding standard currently, achieves the best coding efficiency compared with all the other existing video coding standards. However, the computational complexity of the typical HEVC encoder dramatically increases because of the recursive searching scheme for finding the best coding unit (CU) partitions. In this paper, an adaptive fast CU size decision algorithm for HEVC Intra prediction is proposed based on CU complexity classification (CC) by using machine learning (ML) technology. Firstly, certain image features are extracted to characterize the CU complexity, which has a strong relationship with CU partitions, and then, the support vector machine is employed to analyze and construct the classification model according to the CU complexity. Finally, the proposed adaptive fast CU size decision algorithm, named as CCML, is released based on the complexity classification. The experimental results show that the proposed algorithm could achieve around 60% encoding time reduction for various test video sequences on average with only 1.26% Bjontegaard delta bit rate increase compared with the reference test model HM15.0 of HEVC.

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.

Adaptive Intra Candidate Selection With Early Depth Decision for Fast Intra Prediction in HEVC

To better exploit spatial correlations in a video frame, the High Efficiency Video Coding (HEVC) standard has adopted a great many more intra prediction modes than H.264/AVC. As a result, the complexity of rate-distortion-optimized (RDO) HEVC intra mode selection is very high. Many techniques have been proposed to expedite the intra mode selection process to achieve a better overall tradeoff between complexity and RD performance. In this paper, two novel techniques for adaptive intra mode candidate selection and bidirectional depth search algorithms are utilized to accelerate intra prediction. The proposed techniques show an average of 63% (up to 67%) time saving with only 1% BD-Rate increase, outperforming most of existing intra prediction algorithms.

An Adaptive Mode Decision Algorithm Based on Video Texture Characteristics for HEVC Intra Prediction

The latest High Efficiency Video Coding (HEVC) standard could achieve the highest coding efficiency compared with the existing video coding standards. To improve the coding efficiency of the intra frame, a quad-tree-based variable block size coding structure that is flexible to adapt to various texture characteristics of images and up to 35 intra-prediction modes for each prediction unit (PU) is adopted in HEVC. However, the computational complexity is increased dramatically because all the possible combinations of the mode candidates are calculated in order to find the optimal rate distortion cost using the Lagrange multiplier. To alleviate the encoder computational load, this paper proposes an adaptive mode decision algorithm based on texture complexity and direction for HEVC intra prediction. First, an adaptive coding unit selection algorithm according to each depth levels' texture complexity is presented to filter out unnecessary coding block. Then, the original redundant mode candidates for each PU are reduced according to its texture direction. The simulation results show that the proposed algorithm could reduce around 56% encoding time on average while maintaining the encoding performance efficiently with only a 1.0% increase in BD-rate compared with the test model HM16 of HEVC.

Adaptive Block Coding Order for Intra Prediction in HEVC

In this paper, an adaptive block coding order for intra prediction is proposed. Modern video coding standards, including the most recent High Efficiency Video Coding (HEVC), utilize fixed scan orders in processing blocks during intra coding. However, the fixed scan orders typically result in residual blocks with noticeable edge patterns. That means the fixed scan orders cannot fully exploit the content-adaptive spatial correlations between adjacent blocks, thus the bitrate after compression tends to be large. To reduce the bitrate induced by inaccurate intra prediction, the proposed approach adaptively chooses both the block and subblock coding orders by minimizing the coding cost. Specifically, determining the block coding order is formulated as a traveling salesman problem that is solved using dynamic programming. Besides the block coding order, we also design the subblock coding order in each block with an adaptive manner. The experimental results demonstrate a Bjontegaard-Delta-rate reduction of up to 4.4% compared with HEVC anchor.

Bottom‐up pruning algorithm for intra‐prediction in high‐efficiency video coding

Intra-prediction in high-efficiency video coding (HEVC), in comparison with previous video coding standards, provides higher coding efficiency, at the expense of increased computational complexity. The coding efficiency is increased, mainly due to the various transform block sizes and increased number of modes. An efficient bottom-up pruning algorithm for mode decision is presented, to reduce the computational load of intra-prediction. Experimental results show that the proposed algorithm achieves about 39% reduction in mode decision time in intra-prediction, compared with the HEVC test model 11.0, with negligible coding gain degradation.

Efficient parallel HEVC intra‐prediction on many‐core processor

High-efficiency video coding (HEVC) is the state-of-the-art video coding standard, which adopts more complicated and time-consuming intra-prediction (IP) modes. Many-core processors are good candidates for speeding up HEVC IP in the case that HEVC IP can provide sufficient parallelism. Proposed is an efficient parallel framework for HEVC IP. Experiments show that the proposed method dramatically accelerates more than the state-of-the-art parallel method.