ADAPTIVE REVERSIBLE DATA HIDING METHOD USING BITMAP CLASSIFICATION STRATEGY FOR AMBTC-COMPRESSED IMAGES

Abstract

Reversible data hiding (RDH) has attracted significant attention from researchers. Absolute Moment Block Truncation Coding (AMBTC) compressed image is a popular cover medium in RDH schemes due to its simplicity and high compression ratio. Nevertheless, most AMBTC-based RDH schemes produce stego images that cannot be decoded by standard AMBTC decoders because they use reconstructed pixels to embed data. Although some AMBTC-based RDH schemes generate stego images compatible with AMBTC decoders, their embedding capacity (EC) is limited. To address this issue, in this paper, a high-capacity AMBTC-based RDH scheme compatible with standard decoders is proposed. In this scheme, an effective bitmap classification technique is developed and employed to ensure reversibility and enhance the embedding payload. Furthermore, the proposed scheme provides the ability to adaptively balance the trade-off between hiding the payload and the perceptual quality of stego images by utilizing two predefined thresholds. Experimental results demonstrate that the proposed scheme outperforms related approaches in both embedding payload and visual quality.