sICCI: simplification of content adaptive inter-channel correlation information subnetwork in JPEG AI

As machine learning continues to improve the performance of image compression, there is a high demand for deep learning-based image compression algorithms. The first generation of deep learning-based image compression standard, JPEG AI, has emerged. Compared to the linear transform methods in traditional compression frameworks, deep learning-based image compression codecs use non-linear transform to extract visual features ranging from low to high levels in a large number of training samples, thereby achieving much higher compression performance. JPEG AI aims to explore image encoding methods that are more efficient than existing image codecs. In the JPEG AI official verification model, the Content Adaptive Inter-Channel Correlation Information (ICCI) subnetwork is used to reconstruct compressed images to achieve higher quality, but the complexity and parameter number of this subnetwork are relatively high. To solve this problem, we propose a simplified ICCI (sICCI) based on the Y, U, and V components. Compared to the standard ICCI module in JPEG AI and its lightweight version eICCI, our proposed sICCI significantly reduces network complexity and model parameters while keeping competitive image reconstruction quality.