Accuracy analysis of lossless and lossy disparity map compression

In this paper, we analysed lossless and lossy compression of disparity (depth) images with low range resolution. For that goal, the well-known publicly available Middlebury dataset is used with stereo image pairs, their disparity ground truths and disparity estimations obtained using state-of-the-art algorithms. We show that the WebP image format is suitable for lossless compression of disparity images, with compression ratios between 14 and 56 and a mean compression ratio of 20. Much higher compression ratios, better than 60, can be achieved using lossy image compression HEIC algorithm, with acceptable reduction of the disparity map accuracy. This high compression ratio is proportional to the transmission time reduction. (c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

Automated summary

This paper analyzes the lossless and lossy compression of disparity images with low range resolution. The WebP image format is found suitable for lossless compression with an average compression ratio of 20. The HEIC algorithm achieves much higher compression ratios with acceptable reduction of disparity map accuracy.