无需视口生成，直接评估全景图像质量
Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Unified and Generalized Approach
- 跳过视口生成步骤，直接用等距柱状投影图输入
- 在多个数据集上表现优于现有方法，泛化能力更强
- 既可用于全景图也可用于普通图像质量评估
盲态全景图像质量评估（BOIQA）因存储格式多样和用户观看行为差异而面临巨大挑战。现有方法通常分为视口生成与质量预测两步，计算开销大且难以推广至其他视觉内容（如2D平面图像）。本文实验发现，BOIQA可转化为盲态2D图像质量评估（BIQA）问题，从而无需视口生成，缩小了与BIQA的天然差距。为此提出一种新方法：视口无关——直接接收广泛使用的等距柱状投影格式输入；统一——可同时应用于BOIQA与BIQA；泛化性强——在留出测试、跨数据库验证及公认的gMAD竞赛中均表现优异。
原文摘要 · Abstract (English)
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.
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