RQAT-INR: Improved Implicit Neural Image Compression

• URL: http://arxiv.org/abs/2303.03028v1
• Date: Mon, 6 Mar 2023 10:59:45 GMT
• Title: RQAT-INR: Improved Implicit Neural Image Compression
• Authors: Bharath Bhushan Damodaran, Muhammet Balcilar, Franck Galpin, and Pierre Hellier
• Abstract summary: In this research, we show that INR based image has a lower complexity than VAE based approaches. We also propose several improvements for INR-based image and baseline model by a large margin.
• Score: 4.449835214520727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep variational autoencoders for image and video compression have gained significant attraction in the recent years, due to their potential to offer competitive or better compression rates compared to the decades long traditional codecs such as AVC, HEVC or VVC. However, because of complexity and energy consumption, these approaches are still far away from practical usage in industry. More recently, implicit neural representation (INR) based codecs have emerged, and have lower complexity and energy usage to classical approaches at decoding. However, their performances are not in par at the moment with state-of-the-art methods. In this research, we first show that INR based image codec has a lower complexity than VAE based approaches, then we propose several improvements for INR-based image codec and outperformed baseline model by a large margin.

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