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arXiv · 2108.05997

MUSIQ: Multi-scale Image Quality Transformer

Junjie Ke·Qifei Wang·Yilin Wang·Peyman Milanfar·Feng Yang
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Abstract

Image quality assessment (IQA) is an important research topic for understanding and improving visual experience. The current state-of-the-art IQA methods are based on convolutional neural networks (CNNs). The performance of CNN-based models is often compromised by the fixed shape constraint in batch training. To accommodate this, the input images are usually resized and cropped to a fixed shape, causing image quality degradation. To address this, we design a multi-scale image quality Transformer (MUSIQ) to process native resolution images with varying sizes and aspect ratios. With a multi-scale image representation, our proposed method can capture image quality at different granularities. Furthermore, a novel hash-based 2D spatial embedding and a scale embedding is proposed to support the positional embedding in the multi-scale representation. Experimental results verify that our method can achieve state-of-the-art performance on multiple large scale IQA datasets such as PaQ-2-PiQ, SPAQ and KonIQ-10k.

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Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, Feng Yang. 2021-08-12. MUSIQ: Multi-scale Image Quality Transformer. https://arxiv.org/abs/2108.05997

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