Color Correction Preprocessing for Multiview Video Coding

In multiview video, a number of cameras capture the same scene from different viewpoints. There can be significant variations in the color of views captured with different cameras, which negatively affects performance when the videos are compressed with inter-view prediction. In this letter, a method is proposed for correcting the color of multiview video sets as a preprocessing step to compression. Unlike previous work, where one of the captured views is used as the color reference, we correct all views to match the average color of the set of views. Block-based disparity estimation is used to find matching points between all views in the video set, and the average color is calculated for these matching points. A least-squares regression is performed for each view to find a function that will make the view most closely match the average color. Experimental results show that when multiview video is compressed with joint multiview video model, the proposed method increases compression efficiency by up to 1.0 dB in luma peak signal-to-noise ratio (PSNR) compared to compressing the original uncorrected video.

I. Introduction

Multiview video systems have recently received considerable attention from the research community [1]. To capture a 3-D representation of a dynamic scene, multiple video cameras are used, which capture the scene from different viewpoints. Using image-based rendering techniques [2], virtual viewpoints can be created, allowing the user to watch the scene from a range of viewing positions.