Learning End-to-End Lossy Image Compression: A Benchmark

[ arxiv.org/abs/2002.03711 ]

Abstract

Image compression is one of the most fundamental techniques and commonly used applications in the image and video processing field. Earlier methods built a well-designed pipeline, and efforts were made to improve all modules of the pipeline by handcrafted tuning. Later, tremendous contributions were made, especially when data-driven methods revitalized the domain with their excellent modeling capacities and flexibility in incorporating newly designed modules and constraints. Despite great progress, a systematic benchmark and comprehensive analysis of end-to-end learned image compression methods are lacking. In this paper, we first conduct a comprehensive literature survey of learned image compression methods. The literature is organized based on several aspects to jointly optimize the rate-distortion performance with a neural network, i.e., network architecture, entropy model and rate control. We describe milestones in cutting-edge learned image-compression methods, review a broad range of existing works, and provide insights into their historical development routes. With this survey, the main challenges of image compression methods are revealed, along with opportunities to address the related issues with recent advanced learning methods. This analysis provides an opportunity to take a further step towards higher-efficiency image compression. By introducing a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction, we achieve improved rate-distortion performance, especially on high-resolution images. Extensive benchmark experiments demonstrate the superiority of our model in coding efficiency and the potential for acceleration by large-scale parallel computing devices.

Benchmark

Datasets

[Kodak]E. Kodak, "Kodak lossless true color image suite (PhotoCD PCD0992)".
URL: http://r0k.us/graphics/kodak/

[Tecnick] Asuni, Nicola and Giachetti, Andrea, "TESTIMAGES: a Large-scale Archive for Testing Visual Devices and Basic Image Processing Algorithms," Eurographics Italian Chapter Conference, 2014.
URL: https://testimages.org/sampling/
(40 images with resolution 1200x1200 are used.)

[CLIC 19] CLIC: Workshop and Challenge on Learned Image Compression.
URL: http://www.compression.cc/2019/challenge/
(The professional and mobile validation sets are used.)

Methods

Fig.1 Benchmark results on the Kodak Dataset.

[PCS-18] J. Ball´e, "Efficient nonlinear transforms for lossy image compression," in Proc. of Picture Coding Symposium, 2018.
URL: https://github.com/tensorflow/compression

[ICLR-18] J. Ball´e, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston, "Variational image compression with a scale hyperprior," in Proc. of International Conference on Learning Representations, 2018.
URL: https://github.com/tensorflow/compression

[NIPS-18] D. Minnen, J. Ball´e, and G. D. Toderici, "Joint autoregressive and hierarchical priors for learned image compression," in Proc. of Advances in Neural Information Processing Systems, 2018.
URL: https://github.com/tensorflow/compression

[ICLR-19] J. Lee, S. Cho, and S.-K. Beack, "Context adaptive entropy model for end-to-end optimized image compression," in Proc. of International Conference on Learning Representations, 2019.
URL: https://github.com/JooyoungLeeETRI/CA_Entropy_Model

[CVPR-17] G. Toderici, D. Vincent, N. Johnston, S. Jin Hwang, D. Minnen, J. Shor, and M. Covell, "Full resolution image compression with recurrent neural networks," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2017.
URL: https://github.com/nmjohn/models/tree/master/compression

[CVPR-18] F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, and L. Van Gool, "Conditional probability models for deep image compression," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2018.
URL: https://github.com/fab-jul/imgcomp-cvpr

Details

For details please refer to the paper on arXiv.