Multi-Scale Features and Parallel Transformers Based Image Quality Assessment

• URL: http://arxiv.org/abs/2204.09779v1
• Date: Wed, 20 Apr 2022 20:38:23 GMT
• Title: Multi-Scale Features and Parallel Transformers Based Image Quality Assessment
• Authors: Abhisek Keshari, Komal, Sadbhawna, Badri Subudhi
• Abstract summary: We propose a new architecture for image quality assessment using transformer networks and multi-scale feature extraction. Our experimentation on various datasets, including the PIPAL dataset, demonstrates that the proposed integration technique outperforms existing algorithms.
• Score: 0.6554326244334866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increase in multimedia content, the type of distortions associated with multimedia is also increasing. This problem of image quality assessment is expanded well in the PIPAL dataset, which is still an open problem to solve for researchers. Although, recently proposed transformers networks have already been used in the literature for image quality assessment. At the same time, we notice that multi-scale feature extraction has proven to be a promising approach for image quality assessment. However, the way transformer networks are used for image quality assessment until now lacks these properties of multi-scale feature extraction. We utilized this fact in our approach and proposed a new architecture by integrating these two promising quality assessment techniques of images. Our experimentation on various datasets, including the PIPAL dataset, demonstrates that the proposed integration technique outperforms existing algorithms. The source code of the proposed algorithm is available online: https://github.com/KomalPal9610/IQA

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