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What is Efficient Image Compression with TCM about?

The paper presents a novel learned image compression (LIC) framework that combines Convolutional Neural Networks (CNN) and Transformer architectures through a parallel Transformer-CNN Mixture (TCM) block. This approach aims to leverage the strengths of both methods while maintaining controllable complexity, resulting in state-of-the-art rate-distortion performance across multiple datasets. Experimental results indicate that the proposed method significantly outperforms existing LIC techniques, including the

Author

f20221547

Language

EN