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Rhombus Context Based Gradient Estimation for Information Retrieval Using Digital Media

In: Leveraging Emerging Technologies and Analytics for Empowering Humanity, Vol. 1

Author

Listed:
  • Ravi Uyyala

    (Chaitanya Bharathi Institute of Technology (CBIT))

  • S China Ramu

    (Chaitanya Bharathi Institute of Technology (CBIT))

  • R Ravinder Reddy

    (Chaitanya Bharathi Institute of Technology (CBIT))

  • Prabhat Dansena

    (C V Raman Global University)

Abstract

A successful prediction error expansion (PEE) based reversible data hiding (RDH) algorithm requires an useful pixel prediction algorithm. You can find a plethora of pixel prediction methods in books and online. Gradients are the key issue for predicting the current pixel. Nowadays researchers are more focused on gradients for better predicting the current pixel. The gradient can be used for better analyzing the pixel information. In this study, a novel method for improving current pixel prediction is presented employing shades in the image and rhombus context on $$5 \times 5$$ neighborhood. Based on the local complexity (LoCo) of the pixel, An innovative AHBS has been utilized to incorporate additional data while minimizing distortion. Information retrieval process has been experimented using gray scale images. Findings from the experiment show that the proposed approach is superior than other existing approaches. This method can be applied to various business applications where information hiding plays a crucial role.

Suggested Citation

  • Ravi Uyyala & S China Ramu & R Ravinder Reddy & Prabhat Dansena, 2025. "Rhombus Context Based Gradient Estimation for Information Retrieval Using Digital Media," Springer Proceedings in Business and Economics, in: D P Goyal & Suprateek Sarker & Somnath Mukhopadhyay & Basav Roychoudhury & Parijat Upadhyay & Pradee (ed.), Leveraging Emerging Technologies and Analytics for Empowering Humanity, Vol. 1, chapter 4, pages 83-101, Springer.
    • Handle: RePEc:spr:prbchp:978-981-96-2548-2_4
    DOI: 10.1007/978-981-96-2548-2_4
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