scholarly journals Adaptive Content Frame Skipping for Wyner–Ziv-Based Light Field Image Compression

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1798
Author(s):  
Huy PhiCong ◽  
Stuart Perry ◽  
Xiem HoangVan
Keyword(s):  
Image Compression ◽  
Light Field ◽  
High Efficiency ◽  
Incident Light ◽  
Rate Distortion ◽  
Depth Estimation ◽  
Depth Of Field ◽  
High Efficiency Video Coding ◽  
Compression Performance ◽  
Adaptive Content

Light field (LF) imaging introduces attractive possibilities for digital imaging, such as digital focusing, post-capture changing of the focal plane or view point, and scene depth estimation, by capturing both spatial and angular information of incident light rays. However, LF image compression is still a great challenge, not only due to light field imagery requiring a large amount of storage space and a large transmission bandwidth, but also due to the complexity requirements of various applications. In this paper, we propose a novel LF adaptive content frame skipping compression solution by following a Wyner–Ziv (WZ) coding approach. In the proposed coding approach, the LF image is firstly converted into a four-dimensional LF (4D-LF) data format. To achieve good compression performance, we select an efficient scanning mechanism to generate a 4D-LF pseudo-sequence by analyzing the content of the LF image with different scanning methods. In addition, to further explore the high frame correlation of the 4D-LF pseudo-sequence, we introduce an adaptive frame skipping algorithm followed by decision tree techniques based on the LF characteristics, e.g., the depth of field and angular information. The experimental results show that the proposed WZ-LF coding solution achieves outstanding rate distortion (RD) performance while having less computational complexity. Notably, a bit rate saving of 53% is achieved compared to the standard high-efficiency video coding (HEVC) Intra codec.

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 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.

scholarly journals Enhanced Intra Prediction Based on Adaptive Coding Order and Multiple Reference Sets in HEVC

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 703
Author(s):  
Jin Young Lee
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Prediction Method ◽  
Rate Distortion ◽  
Spatial Prediction ◽  
Intra Prediction ◽  
High Efficiency Video Coding ◽  
Adaptive Coding ◽  
Advanced Video Coding ◽  
Multiple Reference

High Efficiency Video Coding (HEVC) is the most recent video coding standard. It can achieve a significantly higher coding performance than previous video coding standards, such as MPEG-2, MPEG-4, and H.264/AVC (Advanced Video Coding). In particular, to obtain high coding efficiency in intra frames, HEVC investigates various directional spatial prediction modes and then selects the best prediction mode based on rate-distortion optimization. For further improvement of coding performance, this paper proposes an enhanced intra prediction method based on adaptive coding order and multiple reference sets. The adaptive coding order determines the best coding order for each block, and the multiple reference sets enable the block to be predicted from various reference samples. Experimental results demonstrate that the proposed method achieves better intra coding performance than the conventional method.

scholarly journals Time Reduction on 3D-HEVC Depth Maps Coding using Static Decision Trees Built Through Data Mining

2019 ◽  
Author(s):  
Mário Saldanha ◽  
Marcelo Porto ◽  
César Marcon ◽  
Luciano Agostini
Keyword(s):  
Data Mining ◽  
Decision Trees ◽  
Processing Time ◽  
High Efficiency ◽  
Rate Distortion ◽  
Depth Map ◽  
High Efficiency Video Coding ◽  
Block Partitioning ◽  
Coding Efficiency ◽  
Depth Maps

This dissertation presents a fast depth map coding for 3D-High Efficiency Video Coding (3D-HEVC) based on static Coding Unit (CU) splitting decision trees. The proposed solution is based on our previous works and avoids the costly Rate-Distortion Optimization (RDO) process for depth maps coding, which evaluates several possibilities of block partitioning and encoding modes for choosing the best one. This coding approach uses data mining and machine learning to extract the correlation among the encoder context attributes and to build the static decision trees. Each decision tree defines if a depth map CU must be split into smaller blocks, considering the encoding context through the evaluation of the CU features and encoder attributes. The results demonstrated that this approach can halve the 3D-HEVC encoder processing time with negligible coding efficiency loss. Besides, the obtained results surpass all related works regarding processing time and coding efficiency. The results reported in this dissertation were published in three journals and two events, besides generate a patent deposit. These products have the master student as the first author.

scholarly journals Rendering Distortion Estimation Model for 3D High Efficiency Depth Coding

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Qiuwen Zhang ◽  
Liang Tian ◽  
Lixun Huang ◽  
Xiaobing Wang ◽  
Haodong Zhu
Keyword(s):  
High Efficiency ◽  
Three Dimensional ◽  
Rate Distortion ◽  
Depth Map ◽  
Depth Image ◽  
High Efficiency Video Coding ◽  
Estimation Model ◽  
Depth Image Based Rendering ◽  
Distortion Estimation ◽  
3D Scene

A depth map represents three-dimensional (3D) scene geometry information and is used for depth image based rendering (DIBR) to synthesize arbitrary virtual views. Since the depth map is only used to synthesize virtual views and is not displayed directly, the depth map needs to be compressed in a certain way that can minimize distortions in the rendered views. In this paper, a modified distortion estimation model is proposed based on view rendering distortion instead of depth map distortion itself and can be applied to the high efficiency video coding (HEVC) rate distortion cost function process for rendering view quality optimization. Experimental results on various 3D video sequences show that the proposed algorithm provides about 31% BD-rate savings in comparison with HEVC simulcast and 1.3 dB BD-PSNR coding gain for the rendered view.

Perceptual feature guided rate distortion optimization for high efficiency video coding

2016 ◽  
Vol 28 (4) ◽  
pp. 1249-1266 ◽  
Author(s):  
Aisheng Yang ◽  
Huanqiang Zeng ◽  
Jing Chen ◽  
Jianqing Zhu ◽  
Canhui Cai
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Rate Distortion ◽  
Rate Distortion Optimization ◽  
High Efficiency Video Coding ◽  
Perceptual Feature

scholarly journals HEVC intra prediction acceleration based on texture direction and prediction unit modes reuse

2014 ◽  
Vol 3 ◽  
Author(s):  
Thaísa Leal da Silva ◽  
Luciano Volcan Agostini ◽  
Luis Alberto da Silva Cruz
Keyword(s):  
High Efficiency ◽  
Prediction Method ◽  
Rate Distortion ◽  
Low Complexity ◽  
Intra Prediction ◽  
High Efficiency Video Coding ◽  
Hierarchical Tree ◽  
The Cost ◽  
Encoding Efficiency ◽  
Hierarchical Tree Structure

The new High Efficiency Video Coding (HEVC) standard achieves higher encoding efficiency when compared to its predecessors such as H.264/AVC. One of the factors responsible for this improvement is the new intra prediction method, which introduces a larger number of prediction directions resulting in an enhanced rate-distortion (RD) performance obtained at the cost of higher computational complexity. This paper proposes an algorithm to accelerate the intra mode decision, reducing the complexity of intra coding. The acceleration procedure takes into account the texture local directionality information and explores the correlation of intra modes across levels of the hierarchical tree structure used in HEVC. Experimental results show that the proposed algorithm provides a decrease of 39.22 and 43.88% in the HEVC intra prediction processing time on average, for all-intra high efficiency (AI-HE) and low complexity (AI-LC) configurations, respectively, with a small degradation in encoding efficiency (BD-PSNR loss of 0.1 dB for AI-HE and 0.8 dB for AI-LC on average).

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