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An Improved Reversible Data Hiding Algorithm Based on Reconstructed Mapping for PVO-k

Yusen Zhang, Haoyun Xu, Jingwen Li

TL;DR

The paper tackles the limited embedding capacity of existing Pixel-Value Ordering (PVO) family RDH methods. It introduces Promoted PPVO-k (PPVO-k) by reconstructing the mapping to enable embedding into more pixels within each block, particularly the top k equal-valued pixels. Experimental results on standard 512x512 grayscale images show substantial capacity gains over PVO, IPVO, and PVO-k, notably a 11,207-bit improvement on Airplane over PVO-k. The work demonstrates significant potential for higher-capacity reversible data hiding and provides a foundation for adaptive mapping strategies in future RDH research.

Abstract

Reversible Data Hiding (RDH) is a practical and efficient technique for information encryption. Among its methods, the Pixel-Value Ordering (PVO) algorithm and its variants primarily modify prediction errors to embed information. However, both the classic PVO and its improved versions, such as IPVO and PVO-k, share a common limitation: their maximum data embedding capacity for a given grayscale image is relatively low. This poses a challenge when large amounts of data need to be embedded into an image. In response to these issues, this paper proposes an improved design targeting the PVO-k algorithm. We have reconstructed the mapping scheme of the PVO-k algorithm to maximize the number of pixels that can embed encrypted information. Experimental validations show that our proposed scheme significantly surpasses previous algorithms in terms of the maximum data embedding capacity. For instance, when embedding information into a grayscale image of an airplane, our method's capacity exceeds that of PVO-k by 11,207 bits, PVO by 8,004 bits, and IPVO by 4,562 bits. The results demonstrate that our algorithm holds substantial advantages over existing methods and introduces innovative mapping ideas, laying a foundation for future research in reversible data hiding in images.

An Improved Reversible Data Hiding Algorithm Based on Reconstructed Mapping for PVO-k

TL;DR

The paper tackles the limited embedding capacity of existing Pixel-Value Ordering (PVO) family RDH methods. It introduces Promoted PPVO-k (PPVO-k) by reconstructing the mapping to enable embedding into more pixels within each block, particularly the top k equal-valued pixels. Experimental results on standard 512x512 grayscale images show substantial capacity gains over PVO, IPVO, and PVO-k, notably a 11,207-bit improvement on Airplane over PVO-k. The work demonstrates significant potential for higher-capacity reversible data hiding and provides a foundation for adaptive mapping strategies in future RDH research.

Abstract

Reversible Data Hiding (RDH) is a practical and efficient technique for information encryption. Among its methods, the Pixel-Value Ordering (PVO) algorithm and its variants primarily modify prediction errors to embed information. However, both the classic PVO and its improved versions, such as IPVO and PVO-k, share a common limitation: their maximum data embedding capacity for a given grayscale image is relatively low. This poses a challenge when large amounts of data need to be embedded into an image. In response to these issues, this paper proposes an improved design targeting the PVO-k algorithm. We have reconstructed the mapping scheme of the PVO-k algorithm to maximize the number of pixels that can embed encrypted information. Experimental validations show that our proposed scheme significantly surpasses previous algorithms in terms of the maximum data embedding capacity. For instance, when embedding information into a grayscale image of an airplane, our method's capacity exceeds that of PVO-k by 11,207 bits, PVO by 8,004 bits, and IPVO by 4,562 bits. The results demonstrate that our algorithm holds substantial advantages over existing methods and introduces innovative mapping ideas, laying a foundation for future research in reversible data hiding in images.
Paper Structure (10 sections, 21 equations, 4 figures)

This paper contains 10 sections, 21 equations, 4 figures.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. .PVO Algorithm
  4. .IPVO Algorithm
  5. .PVO-k Algorithm
  6. PROMOTED PVO-K ALGORITHEM
  7. .The Information Encryption Process of the PPVO-k Algorithm
  8. .Information Extraction and Carrier Image Restoration in the PPVO-k Algorithm
  9. EXPERIMENTAL RESULTS
  10. CONCLUSION

Figures (4)

  • Figure 1: Arrangement of pixels in grayscale images
  • Figure 2: The schematic of the PPVO-k algorithm for encrypted information embedding
  • Figure 3: Six standard 512x512 grayscale images for experiments
  • Figure 4: The Comparison of IPVO, PVO, PVO-k, and PPVO-k for Different Pictures