发表机构
School of Computational and Mathematical Sciences, Cardiff University; Friedrich-Alexander-Universität Erlangen-Nürnberg（卡迪夫大学计算与数学科学学院; 埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对立体全景图像质量评估的挑战，本文提出受预测编码层级启发的PCH指标，经实验验证其性能优于现有最优方法。
AI 中文摘要
立体全景图像（SOIs）为虚拟现实环境中的用户提供了全新的沉浸式体验质量。然而，由于视场可自由变化、双目视觉等诸多因素，开发高效且准确的SOI感知质量评估指标仍具挑战性。本文基于人类视觉系统（HVS）的特性，提出了一种受预测编码层级启发的指标PCH，用于盲/无参考立体全景图像质量评估。受SOI观看过程的启发，所提PCH包含局部 cyclopean 感知模块、全局预测感知模块及视觉质量回归器。首先，观察者从视口浏览不同球形场景，聚合局部视觉信息以推断SOI的感知质量，因此我们提取各类视口，随后进行 cyclopean 转换与显著性检测，以逼近人脑的感知与注意力。局部聚合后，观察者在脑海中推断全局场景，基于双目机制，我们融合左右视图以执行预测编码层级建模。最后，利用视觉质量回归器获取与局部及全局感知线索相关的最终质量分数。大量实验表明，与现有最优质量评估方法相比，所提PCH实现了具有竞争力且持续提升的性能。
英文摘要
Stereoscopic omnidirectional images (SOIs) have provided users with newly immersive quality of experience in virtual reality environments. However, developing efficient and accurate perceptual quality assessment metrics for SOIs remains challenging due to many factors such as freely changeable field of views and binocular vision. In this paper, based on the characteristics of the human visual system (HVS), we propose a Predictive Coding Hierarchy-inspired metric (PCH) for blind/no-reference stereoscopic omnidirectional image quality assessment. Motivated by the viewing process of SOIs, the proposed PCH includes a local cyclopean perception module, a global predictive perception module, and a visual quality regressor. First, observers browse different spherical sceneries from viewports, and aggregate the local visual information to infer the perceptual quality of SOIs. Therefore, we extract various viewports, followed by cyclopean conversion and saliency detection to approach the perception and attention of the human brain. After the local aggregation, viewers then infer the global scene in their minds. Based on the binocular mechanism, we fuse left and right views to perform predictive coding hierarchy modelling. Finally, the visual quality regressor is exploited to obtain the ultimate quality score related to both local and global perceptual cues. Extensive experiments demonstrate that the proposed PCH achieves competitive and consistently improved performance compared with state-of-the-art quality assessment methods.
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