SINCO: A Novel structural regularizer for image compression using implicit neural representations

• URL: http://arxiv.org/abs/2210.14974v1
• Date: Wed, 26 Oct 2022 18:35:54 GMT
• Title: SINCO: A Novel structural regularizer for image compression using implicit neural representations
• Authors: Harry Gao, Weijie Gan, Zhixin Sun, and Ulugbek S. Kamilov
• Abstract summary: Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression. We present structural regularization for INR compression (SINCO) as a novel INR method for image compression.
• Score: 10.251120382395332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression. An image can be compressed by training an INR model with fewer weights than the number of image pixels to map the coordinates of the image to corresponding pixel values. While traditional training approaches for INRs are based on enforcing pixel-wise image consistency, we propose to further improve image quality by using a new structural regularizer. We present structural regularization for INR compression (SINCO) as a novel INR method for image compression. SINCO imposes structural consistency of the compressed images to the groundtruth by using a segmentation network to penalize the discrepancy of segmentation masks predicted from compressed images. We validate SINCO on brain MRI images by showing that it can achieve better performance than some recent INR methods.

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