Joint coding and embedding techniques for MultimediaFingerprinting

Digital fingerprinting protects multimedia content from illegal redistribution by uniquely marking every copy of the content distributed to each user. The collusion attack is a powerful attack where several different fingerprinted copies of the same content are combined together to attenuate or even remove the fingerprints. One major category of collusion-resistant fingerprinting employs an explicit step of coding. Most existing works on coded fingerprinting mainly focus on the code-level issues and treat the embedding issues through abstract assumptions without examining the overall performance. In this paper, we jointly consider the coding and embedding issues for coded fingerprinting systems and examine their performance in terms of collusion resistance, detection computational complexity, and distribution efficiency. Our studies show that coded fingerprinting has efficient detection but rather low collusion resistance. Taking advantage of joint coding and embedding, we propose a permuted subsegment embedding technique and a group-based joint coding and embedding technique to improve the collusion resistance of coded fingerprinting while maintaining its efficient detection. Experimental results show that the number of colluders that the proposed methods can resist is more than three times as many as that of the conventional coded fingerprinting approaches.     

A new algorithm for the embedding of a prediction mechanism into the JPEG2000 coding chain

In this paper, we propose the embedding of a prediction mechanism into a part of the coding structure of JPEG2000 image compression standard, in order to reduce the amount of bits sent to the arithmetic coder, without any significant changes into the standard architecture and without loosing performance. The prediction is based upon an innovative processing of the data structures used by the standard JPEG2000 in progressive coding and the addition of a Prediction Matrix, whose computation does not add any overhead at the decoder side. Experiments are performed to test the efficacy of the prediction mechanism, and results are compared to the standard JPEG2000 and other similar approaches. Tests are documented over a set of well-known images from literature, also against different kinds of added noise. Performance, in terms of saved bits are reported, and a new figure of merit is defined to test the efficiency of Prediction. The results prove that the new proposal overcomes the standard and other related approaches for the entire set of referenced images, with significant gain in synthetic images, also in presence of noise.     

Rotation invariant watermark embedding based on scale-adapted characteristic regions

Rotation invariance is one of the most challenging issues in robust image watermarking. This paper presents two rotation invariant watermark embedding schemes in the non-subsampled contourlet transform (NSCT) domain based on the scale-adapted local regions. Watermark synchronization is achieved using the scale-space feature point based local characteristic regions. The first method embeds a binary watermark sequence by partitioning the local regions in a rotation invariant pattern. The second method embeds a binary watermark image in a content based manner, and the watermark signal is adaptive to the orientation of the image. Both methods achieve rotation invariant embedding by only using the rotation normalizing angle, thus no interpolation operation is performed. Extensive simulation results and comparisons show that the proposed schemes can efficiently resist both signal processing attacks and geometric attacks.     

An adaptive data hiding scheme with high embedding capacity and visual image quality based on SMVQ prediction through classification codebooks

This study exploits the characteristics of image blocks to develop an adaptive data hiding scheme that is based on SMVQ prediction. Since human beings’ eyes are highly sensitive to smooth images, changes in smooth cause great distortion and attract the attention of interceptors. Hence, this study proposes a data embedding scheme for embedding secret data into edge blocks and non-sufficiently smooth blocks. The experimental results show that the proposed scheme improves the quality of the stego-image and the embedding capacity.     

一种基于积分投影的矢量量化器的设计与实现

提出了一种积分投影和矢量量化（VQ）相结合的图像压缩算法,将图像的每一个4×4分块先进行积分投影,然后再与积分投影后的码书进行量化匹配,大大减少运算量和码书存储面积,而图像的质量只有轻微损失。实验结果表明,与普通VQ相比,本文算法的编码速度有很大幅度的提高,而解码图像的峰值信噪比（PSNR）平均仅降低0.25%,对于某些单纯背景的图像,解码后的质量比普通VQ还会有所增加,此算法有很大的应用前景。设计了编码电路,并在FPGA上进行了验证。整个系统最高时钟频率可达78.12 MHz。     

Lossless compression and information hiding in images

This paper presents an image lossless compression and information-hiding schemes based on the same methodology. The methodology presented here is based on the known SCAN formal language for data accessing and processing. In particular, the compression part produces a lossless compression ratio of 1.88 for the standard Lenna, while the hiding part is able to embeds digital information at 12.5% of the size of the original image. Results from various images are also provided.     

ICICLE: A semantic-based retrieval system for WWW images

In this paper, we present ICICLE (Image ChainNet and Incremental Clustering Engine), a prototype system that we have developed to efficiently and effectively retrieve WWW images based on image semantics. ICICLE has two distinguishing features. First, it employs a novel image representation model called Weight ChainNet to capture the semantics of the image content. A new formula, called list space model, for computing semantic similarities is also introduced. Second, to speed up retrieval, ICICLE employs an incremental clustering mechanism, ICC (Incremental Clustering on ChainNet), to cluster images with similar semantics into the same partition. Each cluster has a summary representative and all clusters' representatives are further summarized into a balanced and full binary tree structure. We conducted an extensive performance study to evaluate ICICLE. Compared with some recently proposed methods, our results show that ICICLE provides better recall and precision. Our clustering technique ICC facilitates speedy retrieval of images without sacrificing recall and precision significantly.     

Visually lossless compression for Bayer color filter array using optimized Vector Quantization

Devices using single sensors to capture colour images are cheaper due to high cost of Charge Couple Device (CCD) sensors or Complementary Metal-Oxide Semiconductor (CMOS) sensors. Single sensor devices use Colour Filter Array (CFA) to sample one colour band at every pixel location. Demosaicking process is applied to interpolate the two missing colours from the surrounding. Typically compression is done on the demosaicked images which may not be efficient due to the individual compression of the different colour space. This work investigated compression of raw data before demosaicking and performs demosaicking to reconstruct the R, G, B bands later. A novel Vector Quantization (VQ) technique for encoding the wavelet decomposed image using Modified Artificial Bee Colony (ABC) optimization algorithm is proposed. The proposed technique is compared with Genetic Algorithm based VQ and ABC based quantization and with standard LBG and Lloyd algorithm. Results show higher Peak Signal-to-Noise Ratio (PSNR) indicating better reconstruction.     

Experiments on block-matching techniques for video coding

Video compression is becoming increasingly important with the advent of compression standards and broadband networks. Recently, several block-based motion-estimation algorithms to exploit the temporal redundancies in a video sequence have been reported in the literature. Some of these algorithms tend to be either computationally expensive or to converge to a local optimum. In this paper, we present results for various block-matching techniques and propose a low-complexity block-matching motion-estimation algorithm that is useful for hybrid video coding schemes, including MPEG video. This algorithm consists of a layered structure search, and, unlike other fast block-matching methods, it does not converge to a local optimum. The proposed method employs a novel matching criterion, namely, the modified pixeldifference classification (MPDC), that offers simplicity with other potential advantages.     

Sharing and hiding secret images with size constraint

This paper presents a method for sharing and hiding secret images. The method is modified from the (t,n) threshold scheme. (Comput.Graph. 26(5)(2002)765) The given secret image is shared and n shadow images are thus generated. Each shadow image is hidden in an ordinary image so as not to attract an attacker's attention. Any t of the n hidden shadows can be used to recover the secret image. The size of each stego image (in which a shadow image is hidden) is about 1/t of that of the secret image, avoiding the need for much storage space and transmission time (in the sense that the total size of t stego images is about the size of the secret image). Experimental results indicate that the qualities of both the recovered secret image and the stego images that contain the hidden shadows are acceptable. The photographers who work in enemy areas can use this system to transmit photographs.     

Compressing still and moving images with wavelets

The wavelet transform has become a cutting-edge technology in image compression research. This article explains what wavelets are and provides a practical, nuts-and-bolts tutorial on wavelet-based compression that will help readers to understand and experiment with this important new technology.The work in this paper was supported by Summus, Ltd.     

Comparison of transform coding techniques for two-dimensional arbitrarily shaped images

Envisioned advanced multimedia video services include arbitrarily shaped (AS) image segments as well as regular rectangular images. Image segments of the TV weather report produced by the chromo-key technique [1] and image segments produced by video analysis and image segmentation [2–4] are typical examples of AS image segments. This paper explores efficient intraframe transform coding techniques for general two-dimensional (2D) AS image segments, treating the traditional rectangular images as a special case. In particular, we focus on the transform coding of the partially defined image blocks along the boundary of the AS image segments. We recognize two different approaches — thebrute force transform coding approach and theshape-adaptive transform coding approach. The former fills the uncovered area with the optimal redundant data such that the resulting transform spectrum is compact. A simple but efficient mirror image extension technique is proposed. Once augmented into full image blocks, these boundary blocks can be processed by traditional block-based transform techniques like the popular discrete cosine transform (DCT). In the second approach, we change either the transform basis or the coefficient calculation process adaptively based on the shape of the AS image segment. We propose an efficientshape-projected problem formulation to reduce the dimension of the problem. Existing coding algorithms, such as the orthogonal transform by Gilge [5] and the iterative coding by Kaup and Aach [6], can be interpreted intuitively. We also propose a new adaptive transform based on the same principle as that used in deriving the DCT from the optimal Karhunen-Loeve transform (KLT). We analyze the tradeoff relationship between compression performance, computational complexity, and codec complexity for different coding schemes. Simulation results show that complicated algorithms (e.g., iterative, adaptive) can improve the quality by 5–10 dB at some computational or hardware cost. Alternatively, the simple mirror image extension technique improves the quality by 3–4 dB without any overheads. The contributions of this paper lie in efficient problem formulations, new transform coding techniques, and numerical tradeoff analyses.     

An effective method for still image compression/decompression for transmission on PSTN lines based on modifications of Huffman coding

This paper presents an effective compression method suitable for transmission the still images on public switching telephone networks (PSTN). Since compression algorithm reduce the number of pixels or the gray levels of a source picture, therefore this will lead to the reduction of the amount of memory needed to store the source information or the time necessary for transmitting by a channel with a limited bandwidth. First, we introduced some current standards and finally the lossy DCT-based JPEG compression method is chosen. According to our studies, this method is one of the suitable methods. However, it is not directly applicable for image transmission on usual telephone lines (PSTN). Therefore, it must be modified considerably to be suitable for our purposes. From Shannon’s Information Theory, we know that for a given information source like an image there is a coding technique which permits a source to be coded with an average code length as close as to the entropy of the source as desired. So, we have modified the Huffman coding technique and obtained a new optimized version of this coding, which has a high speed and is easily implemented. Then, we have applied the DCT¹ and the FDCT² for compression of the data. We have analyzed and written the programs in C++ for image compression/decompression, which give a very high compression ratio (50:1 or more) with an excellent SNR.³In this paper, we present the necessary modifications on Huffman coding algorithms and the results of simulations on typical images.     

基于势函数模糊聚类量化的小波图象压缩

基于小波变换的图象压缩是图象压缩的一项成功技术 ,并且具有越来越重要的作用 ,但基于小波变换的图象压缩算法在比特率较低时出现的边缘模糊现象仍然是一个公认的难题 .为了在一定程度上减少比特率较低时 ,出现的边缘模糊现象 ,提出了一种基于势函数模糊聚类量化的新方法 ,用于对经过小波变换分解后所形成的数字图象高频子带小波系数进行量化 .在量化过程中还考虑了高频子带的小波系数的分布特性和高频子带的小波系数对保存边缘、纹理等信息的重要性程度 ,也利用了模糊集合的特性 .实验证明 ,在低比特率下 ,这种方法能较好地保存图象边缘和纹理等信息 ,从而在一定程度上提高了重构图象的主观质量 .该方法在小波图象压缩的模糊聚类量化领域进行了一定的尝试 .     

基于多分辨率级小波变换的图象压缩方法

随着国际互联网的出现,使得越来越多的图象信息传输交流越来越便捷,但传输速度始终是制约网络发展的重要因素,这也使得对图象进行压缩的要求更加迫切。小波变换的良好空间一频率局部化特性,使得原始图象的能量大部分聚集到了低频子带.为了提高图象压缩的效率和重建图象的质量,利用原始图象在小波分解中不同分辨率级能量分布不均匀的特点,提出了一种对各分辨率级进行分级处理的设计方法,即通过对各分解级量化因子的评价,为该级获取一个最佳的量化因子来进行压缩.实验证明,该方法在提高图象的压缩效率和重构质量方面取得了良好的效果.     

The modelling of images for communication in multimedia environments and the evolution from the image signal to the image document

In the context ofimage communication and interchange, to some extent, it comes to philosophical categories, of one tries to explain the term image:The field is heavily influenced by three scientific disciplines: telecommunication engineering, electronical engineering, and computer science. In this situation, where different disciplines of science intersect with different theoretical background, some terms are used in different meanings and in ambiguous ways.This report makes an attempt to illuminate the area of image communication by providing a basic image model, in both mathematical and data structure means. The report gives also a comparative glossary of terms, as they are used by different scientific communities. Different image interchange techniques are described with respect to different image display architectures. Some of the well-known image communication mechanisms and standards are compared with each other.     

Search-by-content of partially occluded images

This paper describes a method for searching images by content that compensates for occlusions in the image. The method extends a search technique proposed by Stone and Li that is based on two criteria – sum of squares differences and average intensity, but the method can be used in conjunction with other criteria, including the normalized correlation coefficient. Image occlusions have a profound impact on template-matching image searches. A typical example of such an occlusion is a cloud over a land mass in an image taken from a satellite. Searches performed without compensation for occlusions are unable to detect matches in positions near the cloud perimeter that are not totally obscured by the cloud. The compensation method introduced here can discover good matches in regions where patterns overlap occluded regions, and would otherwise be missed. The key idea of the algorithm is to perform computations in a way that removes invalid pixels from summations. The enhanced algorithm requires no more than a factor of two increase in storage and computation costs as compared to the Stone–Li algorithm.     

Novel segmentation algorithm in segmenting medical images

The aim of this paper is to develop an effective fuzzy c-means (FCM) technique for segmentation of Magnetic Resonance Images (MRI) which is seriously affected by intensity inhomogeneities that are created by radio-frequency coils. The weighted bias field information is employed in this work to deal the intensity inhomogeneities during the segmentation of MRI. In order to segment the general shaped MRI dataset which is corrupted by intensity inhomogeneities and other artifacts, the effective objective function of fuzzy c-means is constructed by replacing the Euclidean distance with kernel-induced distance. In this paper, the initial cluster centers are assigned using the proposed center initialization algorithm for executing the effective FCM iteratively. To assess the performance of proposed method in comparison with other existed methods, experiments are performed on synthetic image, real breast and brain MRIs. The clustering results are validated using Silhouette accuracy index. The experimental results demonstrate that our proposed method is a promising technique for effective segmentation of medical images.     

Color image compression based on block truncation coding using pattern fitting principle

This paper describes a color image compression technique based on block truncation coding using pattern fitting (BTC-PF). High degree of correlation between the RGB planes of a color image is reduced by transforming them to O₁O₂O₃ planes. Each O_i plane (1?i?3) is then encoded using BTC-PF method. Size of the pattern book and the block size are selected based on the information content of the corresponding plane. The result of the proposed method is compared with that of several BTC based methods and the former is found superior. Though this method is a spatial domain technique, it is also compared with JPEG compression method, which is one of most popular frequency domain techniques. It is found that the performance of the proposed method is a little inferior to that of the JPEG in terms of quality of the reconstructed image. Decoding time is another important criterion where the compressed image is decoded frequently for various purposes. As the proposed method requires negligible decoding time compared to JPEG, the former is preferred over the latter in those cases.     

A data embedding scheme for color images based on genetic algorithm and absolute moment block truncation coding

Recently, embedding a large amount of secret data into gray-level and color images with low distortion has become an important research issue in steganography. In this paper, we propose a data embedding scheme by using a well-known genetic algorithm, block truncation code and modification direction techniques to embed secret data into compression codes of color images to expand the variety of cover media. In the scheme, the common bitmap generation procedure of GA-AMBTC has been modified to speed up the hiding procedure. Two embedding strategies are proposed to hide secret data into the common bitmap and the quantization values in each block of the cover image. Experimental results confirm that the proposed scheme can provide high data capacity with acceptable image quality of the stego-images. Moreover, the compression ratio of the scheme is exactly the same as that of GA-AMBTC so that attackers cannot detect any trace of hidden data from the size of the modified compressed result.