Hybrid Vision Transformer and Convolutional Neural Network for Super-Resolution Image Quality Assessment

Sun, 19 Oct 2025 00:00:00 +0000

本文针对超分辨率图像质量评估（SRIQA）任务，提出一种结合视觉Transformer（ViT）与卷积神经网络（CNN）的混合无参考评估模型。该方法利用ViT提取非局部特征，并通过多阶段自注意力处理图像令牌，再将其重塑为特征图，由CNN编码映射为质量分数，从而同时捕捉局部与非局部信息，克服了传统全参考指标依赖真实图像且可靠性不足的问题。

Vquala 2025 challenge on image super-resolution generated content quality assessment: Methods and results

Sun, 19 Oct 2025 00:00:00 +0000

This paper presents the ISRGC-Q Challenge, built upon the Image Super-Resolution Generated Content Quality Assessment (ISRGen-QA) dataset, and organized as part of the Visual Quality Assessment (VQualA) Competition at the ICCV 2025 Workshops. Unlike existing Super-Resolution Image Quality Assessment (SR-IQA) datasets, ISRGen-QA places a greater emphasis on SR images generated by the latest generative approaches, including Generative Adversarial Networks (GANs) and diffusion models. The primary goal of this challenge is to analyze the unique artifacts introduced by modern super-resolution techniques and to evaluate their perceptual quality effectively. A total of 108 participants registered for the challenge, with 4 teams submitting valid solutions and fact sheets for the final testing phase. These submissions demonstrated state-of-the-art (SOTA) performance on the ISRGen-QA dataset. The …

Xinyu Li | ViLab

Hybrid Vision Transformer and Convolutional Neural Network for Super-Resolution Image Quality Assessment

Vquala 2025 challenge on image super-resolution generated content quality assessment: Methods and results