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A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment

Autor: Wu, Tianhe, Ma, Kede, Liang, Jie, Yang, Yujiu, Zhang, Lei

While Multimodal Large Language Models (MLLMs) have experienced significant advancement in visual understanding and reasoning, their potential to serve as powerful, flexible, interpretable, and text-driven models for Image Quality Assessment (IQA) re

Externí odkaz: http://arxiv.org/abs/2403.10854

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Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment

Autor: Wu, Tianhe, Shi, Shuwei, Cai, Haoming, Cao, Mingdeng, Xiao, Jing, Zheng, Yinqiang, Yang, Yujiu

Blind Omnidirectional Image Quality Assessment (BOIQA) aims to objectively assess the human perceptual quality of omnidirectional images (ODIs) without relying on pristine-quality image information. It is becoming more significant with the increasing

Externí odkaz: http://arxiv.org/abs/2305.10983

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Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network

Autor: Lao, Shanshan, Gong, Yuan, Shi, Shuwei, Yang, Sidi, Wu, Tianhe, Wang, Jiahao, Xia, Weihao, Yang, Yujiu

Image quality assessment (IQA) algorithm aims to quantify the human perception of image quality. Unfortunately, there is a performance drop when assessing the distortion images generated by generative adversarial network (GAN) with seemingly realisti

Externí odkaz: http://arxiv.org/abs/2204.10485

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MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

Autor: Yang, Sidi, Wu, Tianhe, Shi, Shuwei, Lao, Shanshan, Gong, Yuan, Cao, Mingdeng, Wang, Jiahao, Yang, Yujiu

No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception. Unfortunately, existing NR-IQA methods are far from meeting the needs of predicting accurate quality scores

Externí odkaz: http://arxiv.org/abs/2204.08958

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