Improved coefficient recovery and its application for rewritable data embedding

Sii, Alan and Ong, SimYing and Wong, KokSheik (2021) Improved coefficient recovery and its application for rewritable data embedding. Journal of Imaging, 7 (11). ISSN 2313-433X, DOI

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JPEG is the most commonly utilized image coding standard for storage and transmission purposes. It achieves a good rate-distortion trade-off, and it has been adopted by many, if not all, handheld devices. However, often information loss occurs due to transmission error or damage to the storage device. To address this problem, various coefficient recovery methods have been proposed in the past, including a divide-and-conquer approach to speed up the recovery process. However, the segmentation technique considered in the existing method operates with the assumption of a bi-modal distribution for the pixel values, but most images do not satisfy this condition. Therefore, in this work, an adaptive method was employed to perform more accurate segmentation, so that the real potential of the previous coefficient recovery methods can be unleashed. In addition, an improved rewritable adaptive data embedding method is also proposed that exploits the recoverability of coefficients. Discrete cosine transformation (DCT) patches and blocks for data hiding are judiciously selected based on the predetermined precision to control the embedding capacity and image distortion. Our results suggest that the adaptive coefficient recovery method is able to improve on the conventional method up to 27% in terms of CPU time, and it also achieved better image quality with most considered images. Furthermore, the proposed rewritable data embedding method is able to embed 20,146 bits into an image of dimensions 512x512.

Item Type: Article
Funders: Fundamental Research Grant Scheme (FRGS) MoHE Grant under project-Recovery of missing coefficients fundamentals to applications [FRGS/1/2018/ICT02/MUSM/02/2]
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Software Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 26 May 2022 07:10
Last Modified: 26 May 2022 07:10

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