Abstract
We propose a new method for the visual quality assessment of 360-degree (omnidirectional) videos. The proposed method is based on computing multiple spatio-temporal objective quality features on viewports extracted from 360-degree videos. A new model is learnt to properly combine these features into a metric that closely matches subjective quality scores. The main motivations for the proposed approach are that: 1) quality metrics computed on viewports better captures the user experience than metrics computed on the projection domain; 2) the use of viewports easily supports different projection methods being used in current 360-degree video systems; and 3) no individual objective image quality metric always performs the best for all types of visual distortions, while a learned combination of them is able to adapt to different conditions. Experimental results, based on both the largest available 360-degree videos quality dataset and a cross-dataset validation, demonstrate that the proposed metric outperforms state-of-the-art 360-degree and 2D video quality metrics.
Highlights
D RIVEN by the growing interest in virtual and augmented reality, omnidirectional videos are becoming prevalent in many immersive applications, e.g., medicine, education, and entertainment
There are 60 different reference sequences (12 in raw format and others downloaded from YouTube VR channel) and 540 distorted sequences that were rated by 221 participants
We compare our method to Peak Signal-to-Noise Ratio (PSNR), S-PSNR, WS-PSNR MS-Structural Similarity (SSIM), and VMAF, using common criteria for the evaluation of objective quality metrics: Pearson Linear
Summary
D RIVEN by the growing interest in virtual and augmented reality, omnidirectional (or 360-degree) videos are becoming prevalent in many immersive applications, e.g., medicine, education, and entertainment. Omnidirectional videos are spherical signals captured by cameras with a full 360-degree field-of-view (FoV). When consumed via headmounted displays (HMDs) omnidirectional videos allow the user to be immersed in the content. The new immersive features and interactive dimension change the end user perceived quality of experience (QoE) in many ways when compared to traditional videos [2]. To traditional audiovisual multimedia content, methods for assessing the QoE of omnidirectional content plays a central role in shaping processing algorithms and systems, as well as their implementation, optimization, and testing [24]. The visual quality assessment of omnidirectional videos is one of the most important aspects of users’ QoE when consuming such immersive content
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