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MUSIQ: Multi-scale Image Quality Transformer

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arxiv 2108.05997 v1 pith:YQR5ZE2L submitted 2021-08-12 cs.CV

Junjie Ke , Qifei Wang , Yilin Wang , Peyman Milanfar , Feng Yang This is my paper · ORCID
classification cs.CV
keywords imagequalitymulti-scaleembeddingfixedimagesmethodmusiq
verification ladder T0 review T1 audit T2 compute T3 formal
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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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