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.

快速 JPEG 图像压缩算法是许多应用的必要条件，例如高速视频测量系统和数字电影。除了熵编码是一种变长编码方法，似乎更适合顺序实现之外，现有的许多方法都基于GPU并行实现了JPEG压缩。但是，熵编码是 JPEG 压缩系统的重要组成部分，在 CPU 上实现时通常会占用很大一部分时间。为了解决这个问题，我们提出了一种用于并行 JPEG 压缩的高效并行熵编码 (EPEnt) 方法。所提出的方法在三个并行步骤中进行熵编码：编码、移位和填充。具体来说，根据图像成分的不同特点，我们在编码阶段设计了基于线程和基于扭曲的功能，以在保证图像质量的情况下分别进一步提高效率。我们将所提出的方法应用于并行 JPEG 压缩系统，并基于计算统一设备架构 (CUDA) 评估性能。实验结果表明，与顺序实现相比，熵编码的最大加速比可达39倍，且不影响压缩图像质量。同时，在加速比方面，与最先进的并行方法相比，整个JPEG压缩过程的效率至少提高了28%。我们将所提出的方法应用于并行 JPEG 压缩系统，并基于计算统一设备架构 (CUDA) 评估性能。实验结果表明，与顺序实现相比，熵编码的最大加速比可达39倍，且不影响压缩图像质量。同时，在加速比方面，与最先进的并行方法相比，整个JPEG压缩过程的效率至少提高了28%。我们将所提出的方法应用于并行 JPEG 压缩系统，并基于计算统一设备架构 (CUDA) 评估性能。实验结果表明，与顺序实现相比，熵编码的最大加速比可达39倍，且不影响压缩图像质量。同时，在加速比方面，与最先进的并行方法相比，整个JPEG压缩过程的效率至少提高了28%。