HEVC驱动的视差补偿3D全息图像编码提升真实3D体验

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本文主要探讨了"使用HEVC的基于视差补偿的3D全息图像编码"这一主题。3D全息图像是通过模拟和再现真实三维空间中的光线交互，提供一种沉浸式和逼真的3D体验，对于未来的3D电视技术具有巨大的潜力。视差信息作为3D全息图像的关键特性，它反映了观察者在不同视角下的深度感知，对于构建立体效果至关重要。作者提出了一种新颖的编码策略，即在HEVC（High Efficiency Video Coding）框架下，利用视差补偿进行3D全息图像编码。传统的HEVC编码方式通常将当前编码块分割成四个子块进行处理，但这可能会影响视差信息的准确传递。在提出的算法中，为了确保视差的精确性，编码过程采取了向外扩展的方式，而非简单的分割，这样能够更好地保留和利用视差信息，从而提升图像的立体效果。实验结果显示，与原版HEVC标准中的帧内预测和经典的Temporal Motion Prediction (TMP)算法相比，基于视差补偿的3D全息图像编码算法显示出显著的优点。尽管在编码复杂度上有所增加，但这种提升的性能使得这种改进在实际应用中更具吸引力，特别是在对视觉质量要求较高的场景下，如虚拟现实和增强现实应用。该研究的关键词包括"3D全息图像"、"视差补偿"以及"图像预测"，这些都是实现高效、高质量3D全息图像传输的关键要素。这项工作为3D全息图像的编码技术提供了一个创新的解决方案，有助于推动3D电视和相关领域的发展，尤其是在压缩效率和图像质量之间寻找平衡方面，具有重要的理论价值和实际意义。

DISPARITY COMPENSATION BASED 3D HOLOSCOPIC IMAGE CODING USING HEVC

Deyang Liu, Ping An, Ran Ma, Liquan Shen

School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China

Key Laboratory of Advanced Displays and System Application, Ministry of Education, Shanghai, China

ABSTRACT

3D holoscopic image can provide true 3D content by

reproducing the light rays of the 3D scene and is regarded

as a promising technique for future 3D TV. Disparity

information is paramount intrinsic characteristic of the 3D

holoscopic image. In this paper, a disparity compensation

based 3D holoscopic image coding algorithm using HEVC

is put forward. In order to drive an authentic disparity, we

expand the available area outwards in the disparity

matching process instead of dividing the current coding

block into four parts. Experimental results demonstrate that

the proposed method can obtain considerable gains over

original HEVC intra-prediction and the classical TMP

algorithm with acceptable complexity increasing compared

to original HEVC standard.

Index Terms— 3D holoscopic image, disparity

compensation, image prediction, image coding, HEVC

1. INTRODUCTION

Holoscopic imaging, also referred to as integral imaging,

going back to the pioneering work of Lippmann, can

provide true 3D content by reproducing the light rays of the

3D scene, for which it can minimize some uncomfortable

feelings such as eye strain or headache when people focus

on the screen for a long time. In the simplest form, the

holoscopic imaging system with full parallax consists of a

lens array mated to a digital sensor. Each lens of the lens

arrays can capture perspective views of the 3D scene. For

this reason, 3D holoscopic imaging offers 3D feelings for

more than one person, independent of viewer’s positions

and it is regarded as a promising technique for future 3D

TV.

In order to supply immersive experience for multiple

viewers, much higher image resolution is needed to fit high

definition requirements. Consequently, effective coding

tools are desirable for such particular type of content.

High Efficient Video Coding (HEVC) [1], a new

standard for video coding, developed by Joint Collaborative

Team on Video Coding (JCT-VC), has adopted many

advanced encoding tools and can significantly improve the

compression performance of high definition videos with

half of the bit rate saved compared to the H.264/Advanced

Video Coding (AVC) [2] for the same perceptual video

quality. Therefore, HEVC standard promises to improve the

coding efficiency for holoscopic imaging coding.

Several 3D holoscopic image coding schemes have

been proposed in the literatures. In [3-4], a prediction

coding for 3D holoscopic content, combining the flexible

coding tools from HEVC with self-similarity estimation

concept [5] which exploits the special arrangement of 3D

holoscopic image, is proposed. In this scheme, new

prediction modes are added to explore the particular

structure of 3D holoscopic content. This method can

achieve a good performance, but has to modify the bit

stream structure.

In [6] a locally linear embedding (LLE)-based

prediction algorithm is introduced into the HEVC encoder

to handle 3D holoscopic image. In this paper, some

directional intra prediction modes of the HEVC are

replaced by a more efficient predictive framework based on

LLE technology. The main idea of the LLE [7] based

prediction is to estimate the coding block by using a linear

combination of k-nearest neighbors (k-NN) patches,

determined in the causal coded and reconstructed region of

the image.

The algorithms that are mentioned above can be

interpreted as the improved algorithm based on two

classical methods: intra displacement compensation (IDC)

and template matching prediction (TMP). The two

approaches all try to exploit the self-similarities within a

picture. IDC [8] reuses the reconstructed or decoded block

patches to drive a best intra displacement vector, and then

the best matching block patch is used to predict the current

block. The best displacement vector per block must be sent

to the decoder as overhead. TMP [9] obtains the best

candidate prediction block through measuring the similarity

between the surrounding neighboring pixels and the

candidate template patches which are already reconstructed

or decoded within a searching range. Typically the Sum of

Absolute Differences (SAD) or Sum Squared Errors (SSE)

is chosen as the criterion to retrieve those matching blocks

with higher similarity. After finding the best candidate

template patch, the prediction block, therefore, is used to

predict the current coding block. Different from IDC, TMP

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