Efficient Context-Aware Lossy Image Compression

In this paper, we present ECSIC, a novel learned method for stereo image compression. Our proposed method compresses the left and right images in a joint manner by exploiting the mutual information between the images of the stereo image pair using a novel stereo cross attention (SCA) module and two stereo context modules. The SCA module performs cross-attention restricted to the corresponding epipolar lines of the two images and processes them in parallel. The stereo context modules improve the entropy estimation of the second encoded image by using the first image as a context. We conduct an extensive ablation study demonstrating the effectiveness of the proposed modules and a comprehensive quantitative and qualitative comparison with existing methods. ECSIC achieves state-of-theart performance among stereo image compression models on the two popular stereo image datasets Cityscapes and InStereo2k while allowing for fast encoding and decoding, making it highly practical for real-time applications.

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Efficient Context-Aware Lossy Image Compression

Cited by 2 publications

References 1 publication

Deep image compression based on multi-scale deformable convolution

Deep image compression based on multi-scale deformable convolution

SASIC: Stereo Image Compression with Latent Shifts and Stereo Attention

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