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Compactly representing the visual signals is of fundamental importance invarious image/video-centered applications. Although numerous approaches weredeveloped for improving the image and video coding performance by removing theredundancies within visual signals, much less work has been dedicated to thetransformation of the visual signals to another well-established modality forbetter representation capability. In this paper, we propose a new scheme forvisual signal representation that leverages the philosophy of transferablemodality. In particular, the deep learning model, which characterizes andabsorbs the statistics of the input scene with online training, could beefficiently represented in the sense of rate-utility optimization to serve asthe enhancement layer in the bitstream. As such, the overall performance can befurther guaranteed by optimizing the new modality incorporated. The proposedframework is implemented on the state-of-the-art video coding standard (i.e.,versatile video coding), and significantly better representation capability hasbeen observed based on extensive evaluations.

Towards Modality Transferable Visual Information Representation with Optimal Model Compression