Quantization (image processing)

About: Quantization (image processing) is a research topic. Over the lifetime, 7977 publications have been published within this topic receiving 126632 citations.

Security Analysis on Spatial $\pm$ 1 Steganography for JPEG Decompressed Images

Abstract: Although many existing steganalysis works have shown that the spatial ±1 steganography on JPEG pre-compressed images is relatively easier to be detected compared with that on the never-compressed images, most experimental results seem not very convincing since these methods usually assume that the quantization table of the JPEG stegos previously used is known before detection and/or the length of embedded message is fixed. Furthermore, there are just few effective quantitative algorithms for further estimating the spatial modifications. In this letter, we firstly propose an effective method to detect the quantization table from the contaminated digital images which are originally stored as JPEG format based on our recently developed work about JPEG compression error analysis , and then we present a quantitative method to reliably estimate the length of spatial modifications in those gray-scale JPEG stegos by using data fitting technology. The extensive experimental results show that our estimators are very effective, and the order of magnitude of prediction error can remain around measured by the mean absolute difference.

A secure removable visible watermarking for BTC compressed images

Abstract: A novel removable visible watermarking (RVW) algorithm by combining Block Truncation Coding (BTC) and chaotic map (RVWBCM) is presented in this paper. It embeds a visible watermark in the BTC codes of images, namely both the host image and the watermarked image are BTC compressed images. First, the original image is divided into watermarked region and non-watermarked region, and a predicted version of original image can be obtained by predicting pixel values in watermarked region. Second, adaptive embedding factors are computed according to the image features. Third, the watermark is adaptively embedded into two quantization levels of the BTC compressed image in visible manner. Meanwhile, to further prevent illegal watermark removal, original bi-level watermark is encrypted and then losslessly embedded in invisible manner by adjusting the relationship of two quantization levels. At the receiver's end, only authorized users can exactly extract original bi-level watermark according the relationship of two quantization levels of BTC codes and succeed in remove the embedded visible watermark to reconstruct the original image. The experimental results show that this scheme can achieve a good balance between perceptual transparence and the watermark strength (watermark visibility) and can resist common image processing attacks. The proposed algorithm has low complexity and simplicity of implementation due to the use of BTC. It can be applicable to copyright notification and secure access control in mobile communication.

Improved Spread Transform Dither Modulation using a Perceptual Model: Robustness to Amplitude Scaling and JPEG Compression

Abstract: Spread transform dither modulation (STDM) is a form of quantization index modulation (QIM) that is more robust to re-quantization. However, the robustness of STDM to JPEG compression is still very poor and it remains very sensitive to amplitude scaling. Here, we show how a perceptual model that scales linearly with amplitude scaling can be used to (i) provide robustness to amplitude scaling, (ii) reduce the perceptual distortion at the embedder and (iii) significantly improve the robustness to re-quantization.

Application of adaptive constructive neural networks to image compression

Abstract: The objective of the paper is the application of an adaptive constructive one-hidden-layer feedforward neural networks (OHL-FNNs) to image compression. Comparisons with fixed structure neural networks are performed to demonstrate and illustrate the training and the generalization capabilities of the proposed adaptive constructive networks. The influence of quantization effects as well as comparison with the baseline JPEG scheme are also investigated. It has been demonstrated through several experiments that very promising results are obtained as compared to presently available techniques in the literature.

Watermarking-based image authentication robust to JPEG compression

Abstract: A watermarking-based image authentication scheme in the discrete cosine transform (DCT) domain robust to JPEG compression is presented. The binary authentication code is generated from a pseudo-random sequence based on a secret key and a block-dependent feature, protecting the scheme against cut-and-paste attacks. The watermark is embedded in low-frequency DCT coefficients selected by the secret key using a modified quantisation index modulation approach. Before embedding, the selected coefficients are quantised using the JPEG quantisation matrix for a selected quality factor, protecting the scheme against JPEG compression with higher quality factors. Experimental results show that the proposed technique achieves very good watermark imperceptibility and is able to detect and locate malicious attacks with good precision. Compared with other existing schemes, the proposed algorithm achieves better performance regarding false positive and false negative detection rates and in discriminating malicious attacks from JPEG compression.