Content-oriented learned image compression
This addresses the need for more human-centric compression in image processing, though it is incremental as it builds on existing learned compression methods.
The paper tackles the problem of learned image compression by incorporating image semantics and content, as human eyes have varying sensitivities, and achieves competitive subjective results compared to state-of-the-art methods.
In recent years, with the development of deep neural networks, end-to-end optimized image compression has made significant progress and exceeded the classic methods in terms of rate-distortion performance. However, most learning-based image compression methods are unlabeled and do not consider image semantics or content when optimizing the model. In fact, human eyes have different sensitivities to different content, so the image content also needs to be considered. In this paper, we propose a content-oriented image compression method, which handles different kinds of image contents with different strategies. Extensive experiments show that the proposed method achieves competitive subjective results compared with state-of-the-art end-to-end learned image compression methods or classic methods.