The Discrete Wavelet Transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression. Measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that we call DWT uniform quantization noise; it is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r 2 -l , where r is display visual resolution in pixels/degree, and l is the wavelet level. Thresholds increase rapidly with wavelet spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from low-pass to horizontal/vertical to diagonal. We construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.

In this paper, a spread-spectrum-like discrete cosine transform domain (DCT domain) watermarking technique for copyright protection of still digital images is analyzed. The DCT is applied in blocks of 8 8 pixels as in the JPEG algorithm. The watermark can encode information to track illegal misuses. For flexibility purposes, the original image is not necessary during the ownership verification process, so it must be modeled by noise. Two tests are involved in the ownership verification stage: watermark decoding, in which the message carried by the watermark is extracted, and watermark detection, which decides whether a given image contains a watermark generated with a certain key. We apply generalized Gaussian distributions to statistically model the DCT coefficients of the original image and show how the resulting detector structures lead to considerable improvements in performance with respect to the correlation receiver, which has been widely considered in the literature and makes use of the Gaussian noise assumption. As a result of our work, analytical expressions for performance measures such as the probability of error in watermark decoding and probabilities of false alarm and detection in watermark detection are derived and contrasted with experimental results.

In this paper, we address the problem of the performance analysis of image watermarking systems that do not require the availability of the original image during ownership verification. We focus on a statistical approach to obtain models that can serve as a basis for the application of the decision theory to the design of efficient detector structures. Special attention is paid to the possible nonexistence of a statistical description of the original image. Different modeling approaches are proposed for the cases when such a statistical characterization is known and when it is not. Watermarks may encode a message, and the performance of the watermarking system is evaluated using as a measure the probability of false alarm, the probability of detection when the presence of the watermark is tested, and the probability of error when the information that it carries is extracted. Finally, the modeling techniques studied are applied to the analysis of two watermarking schemes, one of them defined in the spatial domain, and the other in the direct cosine transform (DCT) domain. The theoretical results are contrasted with empirical data obtained through experimentation covering several cases of interest. We show how choosing an appropriate statistical model for the original image can lead to considerable improvements in performance

Multimedia authentication techniques are required in order to ensure trustworthiness of multimedia data. They are usually designed based on two kinds of tools: digital signature or watermarking. Digital signature is a non-repudiatible, encrypted version of the message digest extracted from the data. Watermarking techniques consider multimedia data as a communication channel transmitting owner identification or content integrity information. Given an objective for multimedia authentication to reject the crop-and-replacement process and accept content-preserving manipulations, traditional digital signature or watermarking methods cannot be directly applied. In this thesis, we first propose robust digital signature methods that have proved to be useful for such types of content authentication. Also, we have developed a novel semi-fragile watermarking technique to embed the proposed robust digital signatures. We have implemented a unique Self-Authentication-and-Recovery Images (SARI) system, which can accept quantization-based lossy compression to a determined degree without any false alarms and can sensitively detect and locate malicious manipulations. Furthermore, the corrupted areas can be approximately recovered by the information hidden in the other part of the content. The amount of information embedded in our SARI system has nearly reached the theoretical maximum zero-error information hiding capacity of digital images. Watermarking is a promising solution that can protect the copyright of multimedia data through transcoding. A reasonable expectation of applying watermarking techniques for copyright protection is to consider specific application scenarios, because the distortion behavior involved in these cases (geometric distortion and pixel value distortion) could be reasonably predictable. We propose a practical public watermarking algorithm that is robust to rotation, scaling, and/or translation (RST) distortion. This proposed algorithm plays an important role in our design of the public watermarking technique which survives the image print-and-scan process. In addition, we present our original work in analyzing the theoretical watermarking capacity bounds for digital images, based on the information theory and the characteristics of the human vision system. We investigate watermarking capacity in three directions: the zero-error capacity for public watermarking in magnitude-bounded noisy environments, the watermarking capacity based on domain-specific masking effects, and the watermarking capacity issues based on sophisticated Human Vision System models.

A robust watermarking scheme for hiding binary or gray-scale watermarks in digital images is proposed in this chapter. Motivated by the fact that a detector response (a correlation value) only provides a soft evidence for convincing jury in courtroom, embedded watermarks are designed to be visually recognizable after retrieval. To strengthen the existence confidence of a watermark, visually significant transformed components are selected. In addition, a relocation technique is presented to tackle geometric-distortionbased attacks without using any registration scheme. Finally, a semi-public watermark detector which does not require use of the original source is proposed for the purpose of authentication. Experimental results demonstrate that our approach satisfies the common requirements of image watermarking, and that the performance is superb. Keywords: Human visual system Wavelet transform Watermarking Modulation Attacks Corresponding author 1 INTRODUCTION 1.1 WATERMARKING Ow...

DCT image compression. The discrete cosine transform (DCT) is a

Introduction According to the well-known image transform coding paradigm, an appropriate transformation of the signal is carried out prior to the quantization stage to simplify the quantizer design. Given a certain transform (usually a local frequency transform), two intimately related problems must be solved. The first problem is to decide how to distribute quantization levels in the amplitude 1 Partially supported by CICYT projects TIC98-677-C02-02 and TIC 1FD97-279. 19 July 1999 range of each transform coefficient. The second problem is to determine the number of quantization levels that will be devoted to each coefficient. The first is usually known as 1D quantizer design and the second is often referred to as bit allocation. It is widely accepted that for image coding applications that are judged by a human observer, the properties and limitations of the human visual system (HVS) have to be taken into account

This paper involves designing, implementing, and testing of a locally adaptive perceptual masking threshold model for image compression. This model computes, based on the contents of the original images, the maximum amount of noise energy that can be injected at each transform coefficient that results in perceptually distortion-free still images or sequences of images. The adaptive perceptual masking threshold model can be used as a pre-processor to a JPEG compression standard image coder. DCT coefficients less than their corresponding perceptual thresholds can be set to zero before the normal JPEG quantization and Huffman coding steps. The result is an image-dependent gain in the bit rate needed for transparent coding. In an informal subjective test involving 318 still images in the AT&T Bell Laboratory image database, this model provided a gain in bit-rate saving on the order of 10 to 30 %. 1. INTRODUCTION In recent years, JPEG has emerged as a standard method of image compression [...

To embrace the imminent transition from traditional low-contrast video (LDR) content to superior high dynamic range (HDR) content, we propose a novel backward-compatible HDR video compression (HDR MPEG) method. We introduce a compact reconstruction function that is used to decompose an HDR video stream into a residual stream and a standard LDR stream, which can be played on existing MPEG decoders, such as DVD players. The reconstruction function is finely tuned to the content of each HDR frame to achieve strong decorrelation between the LDR and residual streams, which minimizes the amount of redundant information. The size of the residual stream is further reduced by removing invisible details prior to compression using our HDR-enabled filter, which models luminance adaptation, contrast sensitivity, and visual masking based on the HDR content. Designed especially for DVD movie distribution, our HDR MPEG compression method features low storage requirements for HDR content resulting in a 30 % size increase to an LDR video sequence. The proposed compression method does not impose restrictions or modify the appearance of the LDR or HDR video. This is important for backward compatibility of the LDR stream with current DVD appearance, and also enables independent fine tuning, tone mapping, and color grading of both streams.

In JPEG-to-JPEG image watermarking (J2J), the input is a JPEG image file. After watermark embedding, the image is JPEG-compressed such that the output file is also a JPEG file. In this paper, we use the human visual system (HVS) model to estimate the J2J data hiding capacity of JPEG images, or the maximum number of bits that can be embedded in JPEG-compressed images. Watson's HVS model is modified to estimate the just noticeable difference (JND) for DCT coefficients. The amount of modifications on DCT coefficients is limited by JND in order to guarantee the invisibility of the watermark. Our capacity estimation method does not assume any specific watermarking method and thus would apply to any watermarking method in J2J framework.