Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Unified and Generalized Approach

TL;DR

This paper introduces a new viewport-unaware approach for blind omnidirectional image quality assessment that simplifies the process, enhances generalizability, and unifies BIQA and BOIQA tasks without requiring viewport generation.

Contribution

It reformulates BOIQA as a BIQA problem, eliminating viewport generation, and proposes a unified, generalized method applicable to both omnidirectional and planar images.

Findings

The proposed method outperforms existing BOIQA approaches in various validation tests.

It demonstrates better generalizability across different datasets and competitions.

The approach simplifies the assessment process by removing the need for viewport generation.

Abstract

Blind omnidirectional image quality assessment (BOIQA) presents a great challenge to the visual quality assessment community, due to different storage formats and diverse user viewing behaviors. The main paradigm of BOIQA models includes two steps, ie, viewport generation, and quality prediction, which brings an extra computational burden and is hard to generalize to other visual contents (eg, 2D planar image). Thus, in this paper, we make an attempt to solve these issues. First, we experimentally find that BOIQA can be formulated as a blind (2D planar) image quality assessment (BIQA) problem, ie, the first step - viewport generation - is no longer needed, which narrows the natural gap between BOIQA and BIQA. Then, we present a new BOIQA approach, which has three merits: ie, viewport-unaware - it accepts an omnidirectional image in the widely used equirectangular projection format as input without any transformation; unified - it can also be applied to BIQA; and generalized - it shows better generalizability against other competitors. Finally, we validate its promise by held-out test, cross-database validation, and the well-established gMAD competition.