Lightweight Context Model Equipped aiWave in Response to the AVS Call for Evidence on Volumetric Medical Image Coding.

IEEE Trans. Circuits Syst. Video Technol.(2024)

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Abstract
Volumetric medical images are extensively employed in medical diagnosis, treatment, and research, necessitating a significant demand for coding. Currently, JP3D and HEVC are the prevailing coding standards in practical applications. In recent years, volumetric medical image coding has been extensively studied with researches falling into categories such as video-based, learning-based, and learned wavelet-like transform-based methods. However, these methods are plagued with either inadequate performance or excessive complexity. As such, the pursuit for a more efficient method of coding volumetric medical images remains an urgent and critical issue. Recognizing these requirements, the Audio Video coding Standard Workgroup of China (AVS) initialized a volumetric medical image coding standard and issued a Call for Evidence (CFE) in 2022. In response to this CFE, this paper presents an end-to-end volumetric medical image coding framework aiWave-Lite, which is an upgraded version of aiWave. To be more specific, aiWave-Lite integrates a three-dimensional (3-D) context model with enhanced parallelism and an optimized post-processing module. Leveraging the fully reversible 3-D wavelet-like transform, aiWave-Lite supports high-bit-rate lossy and lossless coding simultaneously. Extensive experimental results reveal that aiWave-Lite exhibits outstanding performance in both lossy and lossless coding and satisfies the multiple technical requirements for volumetric medical image coding. Consequently, it is a highly competitive solution within the CFE.
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Key words
Volumetric medical image compression,end-to-end image compression,lossy and lossless coding,3-D wavelet-like transform,lightweight context model
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