Content Adaptive Latents and Decoder for Neural Image Compression
This work addresses the need for more adaptive compression methods in image processing, though it appears incremental as it builds on existing encoder-side updates.
The paper tackled the problem of neural image compression algorithms lacking content adaptability by proposing a framework that improves adaptability in both latents and the decoder, achieving state-of-the-art performance in experiments.
In recent years, neural image compression (NIC) algorithms have shown powerful coding performance. However, most of them are not adaptive to the image content. Although several content adaptive methods have been proposed by updating the encoder-side components, the adaptability of both latents and the decoder is not well exploited. In this work, we propose a new NIC framework that improves the content adaptability on both latents and the decoder. Specifically, to remove redundancy in the latents, our content adaptive channel dropping (CACD) method automatically selects the optimal quality levels for the latents spatially and drops the redundant channels. Additionally, we propose the content adaptive feature transformation (CAFT) method to improve decoder-side content adaptability by extracting the characteristic information of the image content, which is then used to transform the features in the decoder side. Experimental results demonstrate that our proposed methods with the encoder-side updating algorithm achieve the state-of-the-art performance.