scholarly journals Algorithm and Hardware Design of a Fast Intra Frame Mode Decision Module for H.264/AVC Encoders

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Daniel Palomino ◽  
Guilherme Corrêa ◽  
Cláudio Diniz ◽  
Sergio Bampi ◽  
Luciano Agostini ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Hardware Design ◽  
Rate Distortion ◽  
Mode Decision ◽  
Standard Cells ◽  
Prediction Mode ◽  
Order Of Magnitude ◽  
Decision Module ◽  
Operation Frequency ◽  
Frame Mode

In the rate-distortion optimization (RDO), the process of choosing the best prediction mode is performed through exhaustive executions of the whole encoding process, increasing significantly the encoder computational complexity. Considering H.264/AVC intra frame prediction, there are several modes to encode a macroblock (MB). This work proposes an algorithm and the hardware design for a fast intra frame mode decision module for H.264/AVC encoders. The application of the proposed algorithm reduces in more than 10 times the number of encoding iterations for choosing the best intramode when compared with RDO-based decision. The architecture was synthesized to FPGA and achieved an operation frequency of 98 MHz processing more than 300 HD1080p frames per second. With this approach, we achieved one order-of-magnitude performance improvement compared with RDO-based approaches, which is very important not only from the performance but also from the energy consumption perspective for battery-operated devices. In order to compare the architecture with previously published works, we also synthesized it to standard cells. Compared with the best previous results reported, the implemented architecture achieves a complexity reduction of five times, a processing capability increase of 14 times, and a reduction in the number of clock cycles per MB of 11 times.

scholarly journals Fast Prediction Mode Decision in HEVC Using a Pseudo Rate-Distortion Based on Separated Encoding Structure

ETRI Journal ◽  
2016 ◽  
Vol 38 (5) ◽  
pp. 807-817 ◽  
Author(s):  
Jinwuk Seok ◽  
Younhee Kim ◽  
Myungseok Ki ◽  
Hui Yong Kim ◽  
Jin Soo Choi
Keyword(s):  
Rate Distortion ◽  
Mode Decision ◽  
Prediction Mode ◽  
Prediction Mode Decision ◽  
Fast Prediction

A Whole Rate-Distortion Optimize Pronounce Based on Wireless Sensor Network

2013 ◽  
Vol 756-759 ◽  
pp. 890-894 ◽  
Author(s):  
Qing Sheng Yu ◽  
Jian Zhang ◽  
Jin Xiang Peng
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Estimation Method ◽  
Rate Distortion ◽  
Video Encoding ◽  
Mode Decision ◽  
Wireless Sensor ◽  
Block Basis ◽  
Decision Scheme ◽  
Macro Block

Based on the Joint Video Team (JVT) of the ITU-T Video Coding Experts Group VC EG and the IS O/IEC Moving Picture Experts Group MPEG, an RD optimal Macro Block mode decision scheme for Internet error channel streaming is introduced. The scheme employs the luminance Rate Distortion (RD) optimal mode decision scheme so as to take the effects of video encoding distortion and the channel error propagation to get higher error robustness for error transmission. Based on the Wireless Sensor Network, this paper analyzes the data distortion problem when transmitting H.264 coded video stream over error-prone channel. And the authors also have discussed a widely accepted technique that introduces more intra-coded information on macro block basis. Additionally, this paper introduces a simple loss and multiplication factor estimation method, the rate-distortion optimized assessing strategy over the whole situation.

Fuzzy Holoentropy-Based Adaptive Inter-Prediction Mode Selection for H.264 Video Coding

2019 ◽  
Vol 10 (2) ◽  
pp. 42-60
Author(s):  
Srinivas Bachu ◽  
N. Ramya Teja
Keyword(s):  
Video Coding ◽  
Video Compression ◽  
High Performance ◽  
Mode Selection ◽  
Rate Distortion ◽  
Computation Time ◽  
Exhaustive Search ◽  
Inter Prediction ◽  
Major Drawback ◽  
Prediction Mode

Due to the advancement of multimedia and its requirement of communication over the network, video compression has received much attention among the researchers. One of the popular video codings is scalable video coding, referred to as H.264/AVC standard. The major drawback in the H.264 is that it performs the exhaustive search over the interlayer prediction to gain the best rate-distortion performance. To reduce the computation overhead due to exhaustive search on mode prediction process, this paper presents a new technique for inter prediction mode selection based on the fuzzy holoentropy. This proposed scheme utilizes the pixel values and probabilistic distribution of pixel symbols to decide the mode. The adaptive mode selection is introduced here by analyzing the pixel values of the current block to be coded with those of a motion compensated reference block using fuzzy holoentropy. The adaptively selected mode decision can reduce the computation time without affecting the visual quality of frames. Experimentation of the proposed scheme is evaluated by utilizing five videos, and from the analysis, it is evident that proposed scheme has overall high performance with values of 41.367 dB and 0.992 for PSNR and SSIM respectively.

scholarly journals Texture Grouping and Statistical Optimization Based Mode Prediction Decision Algorithm for Fast HEVC Intra Coding

2021 ◽  
Author(s):  
Jianhua Wang ◽  
Feng Lin ◽  
Jing Zhao ◽  
Yongbing Long
Keyword(s):  
Computational Complexity ◽  
Video Coding ◽  
Rate Distortion ◽  
Statistical Optimization ◽  
Optimal Prediction ◽  
Statistical Probability ◽  
Decision Algorithm ◽  
Texture Information ◽  
Prediction Mode ◽  
Probability Optimization

Abstract HEVC (High Efficiency Video Coding), as one of the newest international video coding standard, can achieve about 50% bit rate reduction compared with H.264/AVC (Advanced Video Coding) at the same perceptual quality due to the use of flexible CTU(coding tree unit) structure, but at the same time, it also dramatically adds the higher computational complexity for HEVC. With the aim of reducing the computational complexity, a texture grouping and statistical optimization based mode prediction decision algorithm is proposed for HEVC intra coding in this paper. The contribution of this paper lies in the fact that we successfully use the texture information grouping and statistical probability optimization technology to rapidly determine the optimal prediction mode for the current PU, which can reduce many unnecessary prediction and calculation operations of HCost (Hadamard Cost) and RDCost (Rate Distortion Cost) in HEVC, thus saving much computation complexity for HEVC. Specially, in our scheme, firstly we group 35 intra prediction modes into 5 subsets of candidate modes list according to its texture information of edge in the current PU, and each subset only contains 11 intra prediction modes, which can greatly reduce many traversing number of candidate mode in RMD (Rough Mode Decision) from 35 to 11 prediction modes; Secondly we use the statistical probability of the first candidate modes in candidate modes list as well as MPM selected as the optimal prediction mode to reduce the number of candidate modes in RDO(Rate Distortion Optimization), which can reduce the number of candidate modes from 3+MPM or 8+MPM to 2 candidate modes; At last, we use the number of candidate modes determined above to quickly find the optimal prediction mode with the minimum RDCost by RDO process. As a result, the computational complexity of HEVC can be efficiently reduced by our proposed scheme. And the simulation results of our experiments show that our proposed intra mode prediction decision algorithm based on texture information grouping and statistical probability optimization in this paper can reduce about 46.13% computational complexity on average only at a cost of 0.67% bit rate increase and 0.056db PSNR decline compared with the standard reference HM16.1 algorithm.

Machine Learning-Based Coding Decision Making in H.265/HEVC CTU Division and Intra Prediction

2020 ◽  
Vol 11 (2) ◽  
pp. 41-60
Author(s):  
Wenchan Jiang ◽  
Ming Yang ◽  
Ying Xie ◽  
Zhigang Li
Keyword(s):  
Machine Learning ◽  
Video Coding ◽  
High Efficiency ◽  
Main Tool ◽  
Decision Time ◽  
Mode Decision ◽  
Supervised Machine Learning ◽  
High Efficiency Video Coding ◽  
Neuron Network ◽  
Prediction Mode

High efficiency video coding (HEVC) has been deemed as the newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. In this research project, in compliance with H.265 standard, the authors focused on improving the performance of encode/decode by optimizing the partition of prediction block in coding unit with the help of supervised machine learning. The authors used Keras library as the main tool to implement the experiments. Key parameters were tuned for the model in the convolution neuron network. The coding tree unit mode decision time produced in the model was compared with that produced in the reference software for HEVC, and it was proven to have improved significantly. The intra-picture prediction mode decision was also investigated with modified model and yielded satisfactory results.

Fast P-Frame Mode Decision Algorithm Using Monotonicity of Jmode

2013 ◽  
Vol 28 (2) ◽  
pp. 266-272
Author(s):  
吴银花 WU Yin-hua ◽  
金龙旭 JIN Long-xu ◽  
张柯 ZHANG Ke
Keyword(s):  
Mode Decision ◽  
Decision Algorithm ◽  
Frame Mode
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