A Large-Scale Compressed 360-Degree Spherical Image Database: From Subjective Quality Evaluation to Objective Model Comparison

Abstract

360-degree images/videos have been dramatically increasing in recent years. But the high resolution makes it difficult to be transported, compressed and stored, and thus constrains the development of 360-degree images/videos. Therefore, it is important to study how popular coding technologies influence the quality of 360-degree images. In this paper, we present a study on subjective assessment of compressed 360-degree images and investigate whether existing objective image quality assessment (IQA) methods can effectively evaluate the quality of compressed 360-degree images. We first construct the largest compressed 360-degree image database (CVIQD2018) including 16 source images and 528 compressed ones with three prevailing coding technologies. Then, we implement 16 full reference (FR) IQA metrics, which include 10 traditional IQA metrics for 2D images and 3 PSNR-based metrics for 360-degree images, as well as 5 no reference (NR) IQA metrics and calculate the correlation between each above metric and subjective assessment in terms of three commonly used performance indices. The experiment results reveal structure information, visual saliency information and compensation for geometric distortion are crucial for evaluating the quality of compressed 360-degree images.