RefQSR: Reference-based Quantization for Image Super-Resolution Networks

• URL: http://arxiv.org/abs/2404.01690v1
• Date: Tue, 2 Apr 2024 06:49:38 GMT
• Title: RefQSR: Reference-based Quantization for Image Super-Resolution Networks
• Authors: Hongjae Lee, Jun-Sang Yoo, Seung-Won Jung,
• Abstract summary: Single image super-resolution aims to reconstruct a high-resolution image from its low-resolution observation. Deep learning-based SISR models show high performance at the expense of increased computational costs. We introduce a novel method called RefQSR that applies high-bit quantization to several representative patches and uses them as references for low-bit quantization of the rest of the patches in an image.
• Score: 14.428652358882978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation. Recent deep learning-based SISR models show high performance at the expense of increased computational costs, limiting their use in resource-constrained environments. As a promising solution for computationally efficient network design, network quantization has been extensively studied. However, existing quantization methods developed for SISR have yet to effectively exploit image self-similarity, which is a new direction for exploration in this study. We introduce a novel method called reference-based quantization for image super-resolution (RefQSR) that applies high-bit quantization to several representative patches and uses them as references for low-bit quantization of the rest of the patches in an image. To this end, we design dedicated patch clustering and reference-based quantization modules and integrate them into existing SISR network quantization methods. The experimental results demonstrate the effectiveness of RefQSR on various SISR networks and quantization methods.

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