scholarly journals Rate Distortion Performance of Motion Estimation for High Efficiency Video Coding

2019 ◽  
Vol 8 (2) ◽  
pp. 2855-2860
Keyword(s):  
Computational Complexity ◽  
Motion Estimation ◽  
Video Coding ◽  
High Efficiency ◽  
Search Algorithm ◽  
Rate Distortion ◽  
Objective Evaluation ◽  
Video Sequences ◽  
High Efficiency Video Coding ◽  
Different Types

The contemporary coding standard for video is High Efficiency Video Coding Standard (HEVC). It’s introduced by ITU-T (International Telegraph Union) and Joint Collaborative Team on Video Coding (JCT-VC). HEVC attains the requirement of video storage and transmission with high resolution. Although it requires the high amount of computational complexity. Motion Vectors are determined with motion estimation analysis; it is implemented with different types of algorithm. In this paper, Motion Estimation Process is implementing with the content split block search algorithm. It improves Peak Signal Noise Ratio (PSNR) than to the existing algorithms. The Objective evaluation has been performed with various video sequences such as BQ Terrace and also improved PSNR.

scholarly journals Motion Estimation in HEVC/H.265: Metaheuristic Approach to Improve the Efficiency

2021 ◽  
Vol 12 (1) ◽  
pp. 59
Author(s):  
Khwaja Humble Hassan ◽  
Shahzad Ahmad Butt
Keyword(s):  
Motion Estimation ◽  
Video Coding ◽  
High Efficiency ◽  
Optimization Problems ◽  
Search Algorithm ◽  
High Efficiency Video Coding ◽  
High Definition ◽  
The Social ◽  
Test Zone ◽  
Encoding Efficiency

An ever increasing use of digital video applications such as video telephony, broadcast and the storage of high and ultra-high definition videos has steered the development of video coding standards. The state of the art video coding standard is High Efficiency Video Coding (HEVC) or otherwise known as H.265. It promises to be 50 percent more efficient than the previous video coding standard H.264. Ultimately, H.265 provides significant improvement in compression at the expense of computational complexity. HEVC encoder is very complex and 50 percent of the encoding consists of Motion Estimation (ME). It uses a Test Zone (TZ) fast search algorithm for its motion estimation, which compares a block of pixels with a few selected blocks in the search region of a referenced frame. However, the encoding time is not suitable to meet the needs of real time video applications. So, there is a requirement to improve the search algorithm and to provide comparable results to TZ search to save a substantial amount of time. In our paper, we aim to study the effects of a meta-heuristic algorithm on motion estimation. One such suitable algorithm for this task is the Firefly Algorithm (FA). FA is inspired by the social behavior of fireflies and is generally used to solve optimization problems. Our results show that implementing FA for ME saves a considerable amount of time with a comparable encoding efficiency.

A Fast Encoding Scheme for High Efficiency Video Coding

2020 ◽  
Vol 13 (6) ◽  
pp. 933-941
Author(s):  
Mohammad Barr
Keyword(s):  
Computational Complexity ◽  
Video Coding ◽  
Video Compression ◽  
High Efficiency ◽  
Rate Distortion ◽  
Unit Size ◽  
High Efficiency Video Coding ◽  
Selection Scheme ◽  
Compression Performance ◽  
Size Selection

Background: High-Efficiency Video Coding (HEVC) is a recent video compression standard. It provides better compression performance compared to its predecessor, H.264/AVC. However, the computational complexity of the HEVC encoder is much higher than that of H.264/AVC encoder. This makes HEVC less attractive to be used in real-time applications and in devices with limited resources (e.g., low memory, low processing power, etc.). The increased computational complexity of HEVC is partly due to its use of a variable size Transform Unit (TU) selection algorithm which successively performs transform operations using transform units of different sizes before selecting the optimal transform unit size. In this paper, a fast transform unit size selection method is proposed to reduce the computational complexity of an HEVC encoder. Methods: Bayesian decision theory is used to predict the size of the TU during encoding. This is done by exploiting the TU size decisions at a previous temporal level and by modeling the relationship between the TU size and the Rate-Distortion (RD) cost values. Results: Simulation results show that the proposed method achieves a reduction of the encoding time of the latest HEVC encoder by 16.21% on average without incurring any noticeable compromise on its compression efficiency. The algorithm also reduces the number of transform operations by 44.98% on average. Conclusion: In this paper, a novel fast TU size selection scheme for HEVC is proposed. The proposed technique outperforms both the latest HEVC reference software, HM 16.0, as well as other state-of-the-art techniques in terms of time-complexity. The compression performance of the proposed technique is comparable to that of HM 16.0.

scholarly journals A Motion Estimation based Algorithm for Encoding Time Reduction in HEVC

2022 ◽  
Vol 72 (1) ◽  
pp. 56-66
Author(s):  
S. Karthik Sairam ◽  
P. Muralidhar
Keyword(s):  
Motion Estimation ◽  
Video Coding ◽  
Video Compression ◽  
High Efficiency ◽  
Search Algorithm ◽  
Video Quality ◽  
Search Pattern ◽  
Absolute Difference ◽  
High Efficiency Video Coding ◽  
Search Patterns

High Efficiency Video Coding (HEVC) is a video compression standard that offers 50% more efficiency at the expense of high encoding time contrasted with the H.264 Advanced Video Coding (AVC) standard. The encoding time must be reduced to satisfy the needs of real-time applications. This paper has proposed the Multi- Level Resolution Vertical Subsampling (MLRVS) algorithm to reduce the encoding time. The vertical subsampling minimizes the number of Sum of Absolute Difference (SAD) computations during the motion estimation process. The complexity reduction algorithm is also used for fast coding the coefficients of the quantised block using a flag decision. Two distinct search patterns are suggested: New Cross Diamond Diamond (NCDD) and New Cross Diamond Hexagonal (NCDH) search patterns, which reduce the time needed to locate the motion vectors. In this paper, the MLRVS algorithm with NCDD and MLRVS algorithm with NCDH search patterns are simulated separately and analyzed. The results show that the encoding time of the encoder is decreased by 55% with MLRVS algorithm using NCDD search pattern and 56% with MLRVS using NCDH search pattern compared to HM16.5 with Test Zone (TZ) search algorithm. These results are achieved with a slight increase in bit rate and negligible deterioration in output video quality.

Real-time motion estimation diamond search algorithm for the new high efficiency video coding on FPGA

2017 ◽  
Vol 94 (2) ◽  
pp. 259-276 ◽  
Author(s):  
Randa Khemiri ◽  
Hassan Kibeya ◽  
Hassen Loukil ◽  
Fatma Ezahra Sayadi ◽  
Mohamed Atri ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Video Coding ◽  
Real Time ◽  
High Efficiency ◽  
Search Algorithm ◽  
High Efficiency Video Coding ◽  
Time Motion ◽  
Diamond Search ◽  
Diamond Search Algorithm

scholarly journals Adaptive CU Split Decision Based on Deep Learning and Multifeature Fusion for H.266/VVC

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jinchao Zhao ◽  
Yihan Wang ◽  
Qiuwen Zhang
Keyword(s):  
Deep Learning ◽  
Video Coding ◽  
High Efficiency ◽  
Texture Classification ◽  
Rate Distortion ◽  
Classification Model ◽  
High Efficiency Video Coding ◽  
Fast Encoding ◽  
Training Samples ◽  
Coding Unit

With the development of technology, the hardware requirement and expectations of user for visual enjoyment are getting higher and higher. The multitype tree (MTT) architecture is proposed by the Joint Video Experts Team (JVET). Therefore, it is necessary to determine not only coding unit (CU) depth but also its split mode in the H.266/Versatile Video Coding (H.266/VVC). Although H.266/VVC achieves significant coding performance on the basis of H.265/High Efficiency Video Coding (H.265/HEVC), it causes significantly coding complexity and increases coding time, where the most time-consuming part is traversal calculation rate-distortion (RD) of CU. To solve these problems, this paper proposes an adaptive CU split decision method based on deep learning and multifeature fusion. Firstly, we develop a texture classification model based on threshold to recognize complex and homogeneous CU. Secondly, if the complex CUs belong to edge CU, a Convolutional Neural Network (CNN) structure based on multifeature fusion is utilized to classify CU. Otherwise, an adaptive CNN structure is used to classify CUs. Finally, the division of CU is determined by the trained network and the parameters of CU. When the complex CUs are split, the above two CNN schemes can successfully process the training samples and terminate the rate-distortion optimization (RDO) calculation for some CUs. The experimental results indicate that the proposed method reduces the computational complexity and saves 39.39% encoding time, thereby achieving fast encoding in H.266/VVC.

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