论文标题
对CPU和GPU上新兴的学习图像压缩的全面复杂性评估
Comprehensive Complexity Assessment of Emerging Learned Image Compression on CPU and GPU
论文作者
论文摘要
学习的压缩（LC）是使用深神经网络压缩图像和视频内容的新兴技术。尽管是新的，但LC方法已经获得了与最新图像压缩相当的压缩效率，例如HEVC甚至VVC。但是，现有的解决方案通常需要巨大的计算复杂性，这阻碍了其在国际标准或产品中的采用。本文提供了几种著名方法的全面复杂性评估，这些方法阐明了此问题，并通过提出关键发现来指导该领域的未来发展。为此，在CPU和GPU平台上对编码和解码进行了六种现有方法。复杂性的各个方面，例如总体复杂性，每个编码模块的份额，操作数量，参数数量，最苛刻的GPU内核以及内存要求，并在Kodak数据集上进行了比较。报告的结果（1）量化了LC方法的复杂性，（2）相当比较不同的方法，（3）工作的主要贡献是识别和量化影响复杂性的关键因素。
Learned Compression (LC) is the emerging technology for compressing image and video content, using deep neural networks. Despite being new, LC methods have already gained a compression efficiency comparable to state-of-the-art image compression, such as HEVC or even VVC. However, the existing solutions often require a huge computational complexity, which discourages their adoption in international standards or products. This paper provides a comprehensive complexity assessment of several notable methods, that shed light on the matter, and guide the future development of this field by presenting key findings. To do so, six existing methods have been evaluated for both encoding and decoding, on CPU and GPU platforms. Various aspects of complexity such as the overall complexity, share of each coding module, number of operations, number of parameters, most demanding GPU kernels, and memory requirements have been measured and compared on Kodak dataset. The reported results (1) quantify the complexity of LC methods, (2) fairly compare different methods, and (3) a major contribution of the work is identifying and quantifying the key factors affecting the complexity.
