Rethinking Semantic Image Compression: Scalable Representation With Cross-Modality Transfer
Pingping Zhang, Shiqi Wang, Meng Wang, Jiguo Li, Xu Wang, Sam Kwong
Abstract
This article proposes the scalable cross-modality compression (SCMC) paradigm, in which the image compression problem is further cast into a representation task by hierarchically sketching the image with different modalities. Herein, we adopt the conceptual organization philosophy to model the overwhelmingly complicated visual patterns, based upon the semantic, structure, and signal level representation accounting for different tasks. The SCMC paradigm that incorporates the representation at different granularities supports diverse application scenarios, such as high-level semantic communication and low-level image reconstruction. The decoder, which enables the recovery of the visual information, benefits from the scalable coding based upon the semantic, structure, and signal layers. Qualitative and quantitative results demonstrate that the SCMC can convey accurate semantic and perceptual information of images, especially at low bitrates, and promising rate-distortion performance has been achieved compared to state-of-the-art methods. The code will be available online <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ppingzhang/SCMC</uri> .