通过基于BM3D的利用视图间相关性的去噪提高光场压缩效率。

Improved Light Field Compression Efficiency through BM3D-Based Denoising Using Inter-View Correlation.

作者信息

Jin You-Na, Rhee Chae-Eun

机构信息

Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea.

Department of Information and Communication Engineering, Inha University, Incheon 22212, Korea.

出版信息

Sensors (Basel). 2021 Apr 21;21(9):2919. doi: 10.3390/s21092919.

DOI:10.3390/s21092919

PMID:33919367

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8122607/

Abstract

Multi-view or light field images have recently gained much attraction from academic and commercial fields to create breakthroughs that go beyond simple video-watching experiences. Immersive virtual reality is an important example. High image quality is essential in systems with a near-eye display device. The compression efficiency is also critical because a large amount of multi-view data needs to be stored and transferred. However, noise can be easily generated during image capturing, and these noisy images severely deteriorate both the quality of experience and the compression efficiency. Therefore, denoising is a prerequisite to produce multi-view-based image contents. In this paper, the structural characteristics of linear multi-view images are fully utilized to increase the denoising speed and performance as well as to improve the compression efficiency. Assuming the sequential processes of denoising and compression, multi-view geometry-based denoising is performed keeping the temporal correlation among views. Experimental results show the proposed scheme significantly improves the compression efficiency of denoised views up to 76.05%, maintaining good denoising quality compared to the popular conventional denoise algorithms.

摘要

多视图或光场图像最近在学术和商业领域备受关注，以创造超越简单视频观看体验的突破。沉浸式虚拟现实就是一个重要例子。在带有近眼显示设备的系统中，高图像质量至关重要。压缩效率也很关键，因为需要存储和传输大量的多视图数据。然而，在图像采集过程中很容易产生噪声，这些有噪声的图像会严重降低体验质量和压缩效率。因此，去噪是生成基于多视图的图像内容的先决条件。在本文中，充分利用线性多视图图像的结构特征来提高去噪速度和性能，并提高压缩效率。假设去噪和压缩的顺序过程，基于多视图几何的去噪在保持视图之间的时间相关性的情况下进行。实验结果表明，与流行的传统去噪算法相比，该方案显著提高了去噪视图的压缩效率，最高可达76.05%，同时保持了良好的去噪质量。