Self-embedding reversible color-to-grayscale conversion with watermarking feature

Abstract

This paper presents a self-embedding reversible color-to-grayscale conversion (RCGC) algorithm that makes good use of deep learning, vector quantization, and halftoning techniques to achieve its goals. By decoupling the luminance information of a pixel from its chrominance information, it explicitly controls the luminance error of both the conversion outputs and their corresponding reconstructed color images. It can also alleviate the burden of the deep learning network used to restore the embedded chrominance information during the reconstruction of the color image. Luminance-guided chrominance quantization and checkerboard-based halftoning are introduced in the paper to encode the chrominance information to be embedded while reference-guided inverse halftoning is proposed to restore the color image. Simulation results verify that its performance is remarkably superior to conventional state-of-art RCGC algorithms in various measures. In the aspect of authentication, embedding the watermark and chrominance information is realized with context-based pixel-wise encryption and a key-based watermark bit positioning mechanism, which makes us possible to locate tampered regions and prevent unauthorized use of the chrominance information.