  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.
