There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

Digital Watermarking

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Quantization is a process that maps a continous or discrete set of values into approximations that belong to a smaller set. Quantization is a lossy: some information about the original data is lost in the process. The key to a successful quantization is therefore the selection of an error criterion – such as entropy and signal-to-noise ratio – and the development of optimal quantizers for this criterion.

Quantization

A robust digital image watermarking scheme that combines image feature extraction and image normalization is proposed. The goal is to resist both geometric distortion and signal processing attacks. We adopt a feature extraction method called Mexican hat wavelet scale interaction. The extracted feature points can survive a variety of attacks and be used as reference points for both watermark embedding and detection. The normalized image of an image (object) is nearly invariant with respect to rotations. As a result, the watermark detection task can be much simplified when it is applied to the normalized image. However, because image normalization is sensitive to image local variation, we apply image normalization to nonoverlapped image disks separately. The disks are centered at the extracted feature points. Several copies of a 16-bit watermark sequence are embedded in the original image to improve the robustness of watermarks. Simulation results show that our scheme can survive low-quality JPEG compression, color reduction, sharpening, Gaussian filtering, median filtering, row or column removal, shearing, rotation, local warping, cropping, and linear geometric transformations.

https://ir.nctu.edu.tw:443/bitstream/11536/27980/1/000181685800006.pdf

A feature-based robust digital image watermarking scheme

A source model describing the relationship between bits, distortion, and quantization step sizes of a large class of block-transform video coders is proposed. This model is initially derived from the rate-distortion theory and then modified to match the practical coders and real image data. The realistic constraints such as quantizer dead-zone and threshold coefficient selection are included in our formulation. The most attractive feature of this model is its simplicity in its final form. It enables us to predict the bits needed to encode a picture at a given distortion or to predict the quantization step size at a given bit rate. There are two aspects of our contribution: one, we extend the existing results of rate-distortion theory to the practical video coders, and two, the nonideal factors in real signals and systems are identified, and their mathematical expressions are derived from empirical data. One application of this model, as shown in the second part of this paper, is the buffer/quantizer control on a CCITT P/spl times/64 k coder with the advantage that the picture quality is nearly constant over the entire picture sequence.

https://ir.nctu.edu.tw:443/bitstream/11536/615/1/A1997WR97700003.pdf

Source model for transform video coder and its application. I. Fundamental theory

A dynamic channel allocation unit (103) specifies a bit rate for each video coder in a set of parallel video coders (102-1,102-N) comprising an overall video coder The dynamic channel allocation unit is supplied from each individual video coder with an average frame quantization step size for the previous frame and the average number of bits produced per pel For the current image frame the dynamic channel allocation unit computes a set of channel sharing factors, ie, the percentage of the total channel bandwidth to be allocated to a particular video coder One channel sharing factor is computed for each individual video coder Individual members of the set of channel sharing factors may be further refined to reflect the prior history of the channel sharing factor for their corresponding coder Additionally, the set of estimates may be normalized to reflect the actual number of bits that can actually be produced by each individual video coder

Method and apparatus for dynamic channel bandwidth allocation among multiple parallel video coders

We present an efficient search technique which minimizes the computations necessary for estimating the motion in video-sequences by the block matching method We also discuss the theoretical basis for conducting such a reduced search by our technique We then present two algorithms which employ the proposed technique for estimating the motion typical of video-conferencing environment Next, the results of computer simulations on a real video-sequence are included which demonstrate the effectiveness of the proposed technique Finally, the results of a study of statistical properties of block motion-compensated frame difference signals are also summarized, to assist in future choice of a coding strategy for such signals

An efficient block-matching algorithm for motion-compensated coding

A buffer/quantizer controller in a video coder utilizes the average quantization step size employed for the previous frame, the average number of bits produced per pel by the video coder for the image contained in the previous frame and current frame buffer fullness level to obtain an indication of image complexity. A target current frame quantization step size to be employed by the video coder in encoding a current frame is based on the indication of image complexity. Optionally, the target frame quantization step size is adjusted by a mapping procedure, at a predetermined intervals within each frame period, to account for the current buffer fullness. The map employed by the mapping procedure is adjusted in accordance with the target frame quantization step size.

Hsueh-Ming Hang

Papers

A feature-based robust digital image watermarking scheme

Source model for transform video coder and its application. I. Fundamental theory

Method and apparatus for dynamic channel bandwidth allocation among multiple parallel video coders

An efficient block-matching algorithm for motion-compensated coding

Adaptive buffer/quantizer control for transform video coders