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.

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 A Layered Approach for Quality Assessment of DIBR-Synthesized Images

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Rafia Mansoor ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Asma Maqsood
Keyword(s):  
Quality Assessment ◽  
Color Image ◽  
Three Dimensional ◽  
Depth Map ◽  
Poor Quality ◽  
Depth Image ◽  
Multiview Video ◽  
3D Scene ◽  
2D And 3D

Multiview video plus depth (MVD) is a popular video format that supports three-dimensional television (3DTV) and free viewpoint television (FTV). 3DTV and FTV provide depth sensation to the viewer by presenting two views of the same scene but with slightly different angles. In MVD, few views are captured, and each view has the color image and the corresponding depth map which is used in depth image-based rendering (DIBR) to generate views at novel viewpoints. The DIBR can introduce various artifacts in the synthesized view resulting in poor quality. Therefore, evaluating the quality of the synthesized image is crucial to provide an appreciable quality of experience (QoE) to the viewer. In a 3D scene, objects are at a different distance from the camera, characterized by their depth. In this paper, we investigate the effect that objects at a different distance make on the overall QoE. In particular, we find that the quality of the closer objects contributes more to the overall quality as compared to the background objects. Based on this phenomenon, we propose a 3D quality assessment metric to evaluate the quality of the synthesized images. The proposed metric using the depth of the scene divides the image into different layers where each layer represents the objects at a different distance from the camera. The quality of each layer is individually computed, and their scores are pooled together to obtain a single quality score that represents the quality of the synthesized image. The performance of the proposed metric is evaluated on two benchmark DIBR image databases. The results show that the proposed metric is highly accurate and performs better than most existing 2D and 3D quality assessment algorithms.

DMMFast: a complexity reduction scheme for three-dimensional high-efficiency video coding intraframe depth map coding

2015 ◽  
Vol 24 (2) ◽  
pp. 023011 ◽  
Author(s):  
Gustavo Sanchez ◽  
Mário Saldanha ◽  
Gabriel Balota ◽  
Bruno Zatt ◽  
Marcelo Porto ◽  
...  
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Three Dimensional ◽  
Depth Map ◽  
Complexity Reduction ◽  
Reduction Scheme ◽  
High Efficiency Video Coding

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 A Complexity Reduction Scheme for Depth Coding in 3D-HEVC

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 164
Author(s):  
Qiuwen Zhang ◽  
Yihan Wang ◽  
Tao Wei ◽  
Bin Jiang ◽  
Yong Gan
Keyword(s):  
High Efficiency ◽  
Simulation Analysis ◽  
Depth Map ◽  
Complexity Reduction ◽  
High Efficiency Video Coding ◽  
Compression Efficiency ◽  
Reduction Strategies ◽  
Depth Coding ◽  
Depth Compression ◽  
Very High

3D-high efficiency video coding (3D-HEVC) is the next-generation compression standard for multiview system applications, which has recently been approved by MPEG and VCEG as an extension of HEVC. To improve the compression efficiency of depth map, several compression tools have been developed for a better representation depth edges. These supplementary coding tools together with existing prediction modes can achieve high compression efficiency, but require a very high complexity that restricts the encoders from ongoing application. In this paper, we introduce a fast scheme to reduce complexity of depth coding in inter and intramode prediction procedure. A simulation analysis is performed to study intra and intermode distribution correlations in the depth compression information. Based on that correlation, we exploit two complexity reduction strategies, including early SKIP and adaptive intra prediction selection. Experimental results demonstrate that our scheme can achieve a complexity reduction up to 63.0%, without any noticeable loss of compression efficiency.

scholarly journals A Novel High Efficiency Fractal Multiview Video Codec

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Shiping Zhu ◽  
Dongyu Zhao ◽  
Ling Zhang
Keyword(s):  
High Efficiency ◽  
Translational Motion ◽  
Three Dimensional ◽  
Rate Distortion ◽  
Spatial Prediction ◽  
Estimation Algorithm ◽  
Video Data ◽  
Video Codec ◽  
Disparity Estimation ◽  
Multiview Video

Multiview video which is one of the main types of three-dimensional (3D) video signals, captured by a set of video cameras from various viewpoints, has attracted much interest recently. Data compression for multiview video has become a major issue. In this paper, a novel high efficiency fractal multiview video codec is proposed. Firstly, intraframe algorithm based on the H.264/AVC intraprediction modes and combining fractal and motion compensation (CFMC) algorithm in which range blocks are predicted by domain blocks in the previously decoded frame using translational motion with gray value transformation is proposed for compressing the anchor viewpoint video. Then temporal-spatial prediction structure and fast disparity estimation algorithm exploiting parallax distribution constraints are designed to compress the multiview video data. The proposed fractal multiview video codec can exploit temporal and spatial correlations adequately. Experimental results show that it can obtain about 0.36 dB increase in the decoding quality and 36.21% decrease in encoding bitrate compared with JMVC8.5, and the encoding time is saved by 95.71%. The rate-distortion comparisons with other multiview video coding methods also demonstrate the superiority of the proposed scheme.

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.

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