An efficient parallel entropy coding method for JPEG compression based on GPU - Zhu, Fushun; Yan, Hua (The Journal of supercomputing, 2022)
The fast JPEG image compression algorithm is a requisite in many applications such as high-speed video measurement systems and digital cinema. Many existing methods have implemented the JPEG compression in parallel based on GPU except for entropy coding, which is a variable-length coding method and seems like a better fit for sequential implementation. However, entropy coding is an essential part of the JPEG compression system and typically takes up a large proportion of the time when implemented on the CPU. To tackle this problem, we propose an efficient parallel entropy coding (EPEnt) method for parallel JPEG compressing. The proposed method conducts entropy coding in three parallel steps: coding, shifting, and stuffing. Specifically, according to the different characteristics of image components, we devise thread-based and warp-based functions in the coding stage to further improve the efficiency under guaranteeing image quality, respectively. We apply the proposed method to the parallel JPEG compression system and evaluate the performance based on compute unified device architecture (CUDA). The experimental results demonstrate that compared with sequential implementation, the maximum speedup ratio of entropy coding can reach 39 times without affecting compressed images quality. Meanwhile, the whole JPEG compression process efficiency increases by at least 28% compared with state-of-the-art parallel methods in terms of speedup ratio.