Low-complexity Transform Network Architecture for JPEG AI Image Codec

Learning-based image coding schemes, exemplified by JPEG AI, have shown potential by greatly exceeding the conventional image compression standards in rate-distortion (RD) performance. However, their widespread applications are hindered by high decoding complexity, particularly from the upsampling and attention modules. Existing works sought to reduce this complexity, but their solutions are not fully effective, leaving considerable complexity unaddressed. In this paper, we present a simplified transform network architecture that employs an optimized attention module, a streamlined upsampling module, and a pared-down activation function to tackle this issue. Simulation results show that the simplified decoder sees its complexity (measured by kMACs/pixel) reduced by 80% (from 833 to 172), while the gain over Versatile Video Coding (VVC) increases slightly (from 27.3% to 27.5%). Partial methods in this paper have been integrated into the JPEG AI Verification Model (VM) software.