2020/02/09 by Jiaheng Liu, Guo Lu, Liu, Jiaheng +5 · 64 citations
Computer Science · Engineering · #Advanced Data Compression Techniques #Advanced Image Processing Techniques #Artificial intelligence #Composite material #Compression (physics) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #End-to-end principle #FOS: Computer and information sciences #FOS: Electrical engineering #Image (mathematics) #Image and Video Processing (eess.IV) #Image compression #Image processing #Materials science #Video Coding and Compression Technologies #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2002.03370
published in arXiv (Cornell University) (Cornell University) · We will released our code and training data
openalex publication_date 2020/02/09 · arxiv created 2020/05/23 · arxiv updated 2020/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Image compression is a widely used technique to reduce the spatial redundancy in images. Recently, learning based image compression has achieved significant progress by using the powerful representation ability from neural networks. However, the current state-of-the-art learning based image compression methods suffer from the huge computational cost, which limits their capacity for practical applications. In this paper, we propose a unified framework called Efficient Deep Image Compression (EDIC) based on three new technologies, including a channel attention module, a Gaussian mixture model and a decoder-side enhancement module. Specifically, we design an auto-encoder style network for learning based image compression. To improve the coding efficiency, we exploit the channel relationship between latent representations by using the channel attention module. Besides, the Gaussian mixture model is introduced for the entropy model and improves the accuracy for bitrate estimation. Furthermore, we introduce the decoder-side enhancement module to further improve image compression performance. Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework to further improve the video compression performance. Simultaneously, our EDIC method boosts the coding performance significantly while bringing slightly increased computational cost. More importantly, experimental results demonstrate that the proposed approach outperforms the current state-of-the-art image compression methods and is up to more than 150 times faster in terms of decoding speed when compared with Minnen's method. The proposed framework also successfully improves the performance of the recent deep video compression system DVC. Our code will be released at https://github.com/liujiaheng/compression.