RQAT-INR: Improved Implicit Neural Image Compression
This work addresses the need for efficient image compression methods for practical industry applications, though it appears incremental as it builds on existing INR approaches.
The paper tackled the problem of high complexity and energy consumption in deep learning-based image compression by improving implicit neural representation (INR) codecs, resulting in a large margin performance gain over baseline models.
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.