Lossless data hiding in JPEG images with segment coding

Abstract

In this paper, we propose a lossless data hiding scheme in JPEG images. After quantified DCT transform, coefficients have characteristics that distribution in high frequencies is relatively sparse and absolute values are small. To improve encoding efficiency, we put forward an encoding algorithm that searches for a high frequency as terminate point and recode the coefficients above, so spare space is reserved to embed secret data and appended data with no file expansion. Receiver can obtain terminate point through data analysis, extract additional data and recover original JPEG images lossless. Experimental results show that the proposed method has a larger capacity than state-of-the-art works.

A software system for evaluating the quality of aerial photographic unreferenced images

“…In order to test the effectiveness of the software algorithm, the original high-quality images and three reduced-quality images were evaluated respectively [10]. As shown in Figure 2, for the original image, the image quality of the original image is evaluated by blurring, reducing contrast, reducing brightness and other degraded images.…”

Solving Image Processing Critical Problems Using Machine Learning

“…Thus, most of the companies will use the data compression technique in order to run out of space. Machine Learning algorithms like support vector machine combines with the DCT discrete cosine transform achieve the level as compared to the RBF and multilayer preceptor [54,57]. DCT is based on the JPEG compression algorithm one of the most widely used image compression algorithms.…”

“…DCT is based on the JPEG compression algorithm one of the most widely used image compression algorithms. In the resulting value of the matrix it divides the whole image into pixel of squares and apply a discrete cosine function [57].…”

A Reversible Data Hiding Method for AMBTC Compressed Image without Expansion inside Stego Format

“…RDH can be applied not only in spatial domain of cover image by directly modifying the pixels, but also employed in compressed domains. Many recent researches have exploited extending RDH in compressed code stream including vector quantization (VQ) [7] and joint photographic experts group (JPEG) [8], [9].…”