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.