저작권 보호를 위한 DCT 영역에서의 향상된 CRT 기반 영상 워터마킹

Many effective watermarking algorithms have been proposed and implemented for digital images and digital video. However, a few algorithms have been proposed for audio watermarking. This is due to the fact that, human auditory system (HAS) is far more complex and sensitive than human visual system (HVS). In this research work, a new method of embedding image data into the audio signal and additive audio watermarking algorithm based on Discrete Cosine Transformation (DCT) domain is proposed. First, the original audio is transformed into DCT domain. The DCT coefficients are divided into a fixed number of subsections and the energy of each subsection is calculated. Next, watermark is generated from image by image processing algorithm. Watermarks are then embedded into selected peaks of highest energy subsection. Experimental results demonstrate that the watermark is inaudible and this algorithm is robust to common operations of digital audio signal processing, such as noise addition, re-sampling, requantization and so on. To evaluate the performance of the proposed audio watermarking method, subjective and objective quality tests including Bit Error Rate (BER) and Signal to Noise ratio (SNR) are conducted.

Just noticeable difference (JND), which reveals the visibility of our human visual system (HVS), is useful for image/video coding. Due to the content complexity, it is hard to accurately estimate the JND thresholds for different image blocks (e.g., edge and texture). Research on cognitive science indicates that the HVS is adaptive to extract the visual regularities for scene perception and understanding. Inspired by this, we analyze the effect of content complexity on visual masking, and calculate the content complexity with the distribution of orientation. Then, by considering the effect from content complexity, contrast sensitivity function, and luminance adaption, a novel JND model in Discrete Cosine Transform (DCT) domain is proposed. Experimental results demonstrate the effectiveness of the proposed model for JND estimation.

Nowadays, the majority of images are in JPEG and MPEG compressed formats, and JPEG2000 is considered to be the next generation of compression standard due to the high- performance of discrete wavelet transform (DWT). It is time- consuming and occupies too much memory in conventional image retrieval ways. In order to solve these problems, we use grading retrieval techniques to implement image retrieval based on discrete cosine transform (DCT) compressed domain and DWT compressed domain. For image retrieval based on DCT domain, we use color features: color moment and color histogram, to describe content of images and propose a new dynamic color space quantization based on color distribution; For image retrieval based on DWT domain, we use texture features as two level feature vectors. The mean and standard deviation of low frequency sub-band coefficients are used as the first level retrieval. The means and standard deviations of selected high frequency sub-band coefficients are used as the second level retrieval. Furthermore, the third level retrieval is achieved by the fast wavelet histogram. Our experiment results clearly show that the two grading image retrieval algorithms work better than other algorithms: store memory is reduced and retrieval accuracy is improved. Index Terms—Content based image retrieval (CBIR), compressed domain, discrete cosine transform (DCT), discrete wavelet transform (DWT), color features, texture features

At recently, the most of the digital images are stored and transferred in their compressed format using discrete cosine transform (DCT)-based compression technique. DCT is one of the most important data compression technique due to the efforts from Joint Photographic Experts Group(JPEG). Blind steganalysis means how to detect the presence of the messages that are hidden using different types of steganography algorithms and has the ability to detect new unknown steganography algorithms. This paper presents blind steganalysis technique that can reliably detect the existence of messages hidden in JPEG files. In order to save the computation and memory cost, it is desirable to have image processing operations implemented directly in the DCT domain. The proposed method is based on extracting features directly from DCT domain through the analysis of differences between DCT coefficients before and after cropping. The extracted features are prepared as input to Support Vector Machine (SVM) to classify the image as stego (image that contain secret message) or clean (image that does not contain secret message). The experiments performed show that the proposed method yields better classification accuracy compared with other related works.

To meet with the different client end devices and network bandwidth transmission requirements, it is necessary to convert the high definition resolution images and videos to standard definition resolution format. A novel approach to convert image resolution with arbitrary ratios in the DCT (Discrete Cosine Transform) domain is proposed, which exploits the relationship of a block and its subblocks with differing size. It can realize arbitrary unequal ratios in the horizontal and vertical direction, respectively. Unlike the present methods, it does not need upsampling- downsampling process. It can perform downsizing directly to the original data and confirms to the standard decoder. The proposed approach is computationally fast and memory efficient and produces visually better images with higher PSNR compared to the spatial methods.

The aim of image fusion is to combine similar information from multiple images into a single image. The methods which are based on discrete cosine transform (DCT) of image fusion are more competent and time-saving in real-time systems using DCT based standards of still Image. The existing DCT based methods are suffering from some side effects like blurring which can reduce the quality of the output image. To address this issue, the paper proposing new method for image fusion using Gaussian pyramid in DCT domain. The pyramid fusion provides better fusion quality. The execution time is extremely reduced, compare with existing methods. This method can be used for multi model image fusion as well as fusion of complementary images. The algorithm given in proposed system is simple and easy to implement. Also, it could be used for real time applications. The performance of our method is analyzed and compared with other image fusion methods. Experimental results show that there is no difference between the result of our method and water-based image fusion result. But our algorithm is carried out in DCT domain; it is efficient in processing time and simple.

The paper proposes an efficient transient audio coding algorithm based on Discrete Cosine Transform (DCT) and sine extraction with Matching Pursuit (MP), which can be used conveniently in the sinusoidal parametric audio coder. The overlapped input frame is firstly classified into transient and non-transient signal. The transient detection is designed based on the energy ratio of the overlapped subframe to the whole frame. The transient signal can be represented with sinusoidal model in the DCT domain. After DCT processing, the sinusoids are extracted based on MP algorithm. The transient coding parameters contain a series of amplitudes, frequencies and phases in the DCT domain. Finally, a 15.5kbps wide-band transient audio coder is designed and the synthesized transient signal performs a little better than the one coded by MPEG-4 sinusoidal parametric coder.

We propose an efficient method for separable symmetric linear filtering in the discrete cosine transform (DCT) domain. First, separable two-dimensional (2-D) linear filtering is decomposed into the cascade of one-dimensional filtering in the DCT domain. We investigate special characteristics of DCT domain filtering matrices when the filter coefficients are symmetric. Then we present the DCT-domain 2-D filtering method using these characteristics. The proposed method requires a smaller number of multiplications including the typical sparseness of DCT coefficients compared to previous DCT-domain linear filtering methods. Also, the proposed method is composed of simple and regular operations, which would be appropriate for efficient very large scale integration implementation.

Based on luminance and texture masking and frequency sensitivity of human visual system (HVS), a blind watermarking algorithm based on discrete cosine transform (DCT) for color image was proposed. The blue color difference component (Cb) of the color image was 2:1 horizontal down- sampled, and watermark was embedded in the DCT domain by dithering the DCT quantization coefficients. Watermark was first coded by (15,7) Reed-Solomon code, scrambled by spread spectrum code and then embedded in the DCT intermediate frequency coefficients of the host image's Cb component. The experimental results show good results both in watermark transparency and robustness. The algorithm can not only extract watermark blindly but also resist some digital image attacks, such as JPEG compression, noise, filtering, rotating, cropping and rescaling.

Steganography is the art and science of hiding data in different media such as texts, audios, images, and videos. Data hiding techniques are generally divided into two groups: spatial and frequency domain techniques. Spatial domain methods generally have low security and, as a result, are less attractive to researchers. Discrete cosine transform (DCT) is the most common transform domain used in steganography and JPEG compression. Since a large number of the DCT coefficients of JPEG images are zero, the capacity of DCT domain-based steganography methods is not very high. We present a high-capacity method for hiding messages in the DCT domain. We describe the method in two classes where the receiver has and where the receiver does not have the cover image. In each class, we consider three cases for each coefficient. By considering n coefficients, there are 3 n different situations. The method embeds ⌊log 2 3 n ⌋ bits in these n coefficients. We show that the maximum reachable capacity by our method is 58% higher than the other general steganography methods. Experimental results show that the histogram-based steganalysis methods cannot detect the stego images produced by the proposed method while the capacity is increased significantly.

Edge Enhancement is an important image processing method for remote sensing image data. Many images are compressed by JPEG standard which uses the Discrete Cosine Transform (DCT). Manipulating data in the DCT domain is an efficient way to save the computer resources. In this paper, new algorithms for edge enhancement of remote sensing image data in the DCT domain are developed and implemented in three steps: highpass filtering, adding back full or part of gray levels to the original image, and linear contrast stretching. In addition, a method to approximate the minimum (MIN) and maximum (MAX) gray level intensity is presented in the paper, which are the necessary information for contrast stretching. Experimental results show that the quality of images generated by the new algorithms are comparable with that of images generated by the corresponding methods in the spatial domain.

A classification algorithm in the discrete cosine transform (DCT) domain for the classified vector quantization (CVQ) technique is proposed. The classifier employs four DCT coefficients of 4*4 subblock as edge-oriented features. The classifier is designed using a cluster-seeking algorithm to ensure that the centroid of a set of vectors in a class always belong to that class. Since the classification is performed in the DCT domain, this approach can be easily extended to the DCT transform coding technique. Simulation results show that a good visual quality of the coded image at fixed rates in the 0.625-0.813 b/pixel (bpp) range is obtained with comparable complexity. The weighted MSE (WMSE) analysis in conjunction with the proposed classifier is discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Nowadays image authentication plays a vital role for security. Watermarking techniques facilitate to hide the data in carrier image in such a way so that data would become imperceptible. Watermarking can be performed either in spatial domain or in transfer domain. Least Significant Bit (LSB) modification is the simple but non reliable theme, which is performed in spatial domain. In transfer domain, Singular Value Decomposition (SVD), Discrete Cosine Transformation (DCT), Fast Fourier Transformation (FFT), Discrete Wavelet Transformation (DWT) and Fast hadamard Transform (FHT) etc provide robust result. The goal of the paper is to present how such methods are useful to authenticate images and avoid image tampering. In our work, we have discussed block and non block based watermarking scheme performed in DCT domain using SVD. Several experiments are carried out to check the robustness of the algorithm. We conclude that non block based algorithm is superior in terms of visual quality after embedding and extraction, while its inferior in terms of some common signal processing attacks like rotation, scaling, translation etc.

Huge number of images is available on the internet. Efficient and effective retrieval system is needed to retrieve these images by the contents or features of the images like color, texture and shape. This system is called content based image retrieval (CBIR). Conventionally features are extracted from images in pixel domain. But at present almost all images are represented in compressed form using DCT (Discrete Cosine Transformation) blocks transformation. Some critical information is removed in compression and only perceptual information is left which has significant attraction for information retrieval in compressed domain. In this paper we study the problem that how to retrieve perceptual information in compressed domain JPEG such that to improve image retrieval. Our approach is based on quantized histogram statistical texture features in DCT blocks. We show that to get best image retrieval performance by extracting the statistical texture features of quantized histogram in DCT blocks using JPEG compressed format images. Experiments on the Corel animal database using the proposed approach, give results which show that the statistical texture features of histogram are robust in retrieval of images. This shows that texture features in local compression is a significant step for effective image retrieval.

A new global image restoration scheme using discrete cosine transform (DCT) is proposed in this paper. This DCT-based image restoration scheme can be used for inspecting the defects in directional texture surfaces automatically. The input spatial domain image is first transformed into DCT domain. The dominating directions of the textures in the input image will be compacted to orthogonal straight lines, respectively, throughout the direct current (DC) component in the spectrum. The linear primitives associated with high-energy frequency components in the DCT domain are eliminated by reducing them to zero and then transformed back to the spatial domain. This procedure will have all directional textures be blurred and will preserve only local defects if they are initially embedded in the input image. Experiments on a variety of product surfaces with directional texture such as straight, slant, orthogonal, and slant orthogonal linear primitives are given to demonstrate the effectiveness and robustness of the proposed method.