Temporal Inconsistency Guidance for Super-resolution Video Quality Assessment

TL;DR

This paper introduces TIG-SVQA, a novel video quality assessment method that explicitly quantifies and leverages temporal inconsistency to better evaluate super-resolution videos, outperforming existing approaches.

Contribution

The paper proposes a perception-oriented temporal inconsistency quantification method and a novel guidance framework for SR video quality assessment, emphasizing the role of inconsistency in perceived quality.

Findings

Outperforms state-of-the-art VQA methods on SR videos

Effectively localizes inconsistent regions at multiple scales

Improves correlation with human perception in quality assessment

Abstract

As super-resolution (SR) techniques introduce unique distortions that fundamentally differ from those caused by traditional degradation processes (e.g., compression), there is an increasing demand for specialized video quality assessment (VQA) methods tailored to SR-generated content. One critical factor affecting perceived quality is temporal inconsistency, which refers to irregularities between consecutive frames. However, existing VQA approaches rarely quantify this phenomenon or explicitly investigate its relationship with human perception. Moreover, SR videos exhibit amplified inconsistency levels as a result of enhancement processes. In this paper, we propose \textit{Temporal Inconsistency Guidance for Super-resolution Video Quality Assessment (TIG-SVQA)} that underscores the critical role of temporal inconsistency in guiding the quality assessment of SR videos. We first design a perception-oriented approach to quantify frame-wise temporal inconsistency. Based on this, we introduce the Inconsistency Highlighted Spatial Module, which localizes inconsistent regions at both coarse and fine scales. Inspired by the human visual system, we further develop an Inconsistency Guided Temporal Module that performs progressive temporal feature aggregation: (1) a consistency-aware fusion stage in which a visual memory capacity block adaptively determines the information load of each temporal segment based on inconsistency levels, and (2) an informative filtering stage for emphasizing quality-related features. Extensive experiments on both single-frame and multi-frame SR video scenarios demonstrate that our method significantly outperforms state-of-the-art VQA approaches. The code is publicly available at https://github.com/Lighting-YXLI/TIG-SVQA-main.