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

Huy Phicong,Stuart Perry,Xiem Hoangvan

doi:10.3390/electronics9111798

Abstract

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.

Highlights

Light field (LF) rendering is known as an attractive form of image-based rendering (IBR) [1,2], which collects immense amounts of image data due to the intensity of light rays traveling in every angle at every point in 3D space being captured [3]
This process is defined as the Plenoptic function, PLF, and explains the huge amount of data stored in each LF image, as an LF image can include 7D information (PLF (x, y, z, θ, ∅, γ, t)) [3]
The results show significant compression performance compared to intra coding while maintaining the random access capabilities

Summary

Introduction

The LF image data include information such as the location or point ( x, y, z), the angle or direction (θ, ∅), the wavelength (γ), and the time (t) for light rays captured in a scene. This process is defined as the Plenoptic function, PLF , and explains the huge amount of data stored in each LF image, as an LF image can include 7D information (PLF (x, y, z, θ, ∅, γ, t)) [3]. Each SAI corresponds to a captured image from a scene from a particular point of view, which can vary slightly between two different SAIs [4]. Information about the parallax and depth of an image scene can be Electronics 2020, 9, 1798; doi:10.3390/electronics9111798 www.mdpi.com/journal/electronics

Methods

Results

Conclusion