Digital image processing remains a challenging domain of programming. There are several transforms that do image **transformation**. Image compression is very important for efficient transmission and storage of images . Demand for communication of multimedia data through the telecommunications network and accessing the multimedia data through Internet is growing explosively. Many algorithms and VLSI architectures for the fast computation of DCT have been proposed. A **discrete** **cosine** transform (DCT) expresses a sequence of finitely many data points in terms of a sum of **cosine** functions oscillating at different frequencies. DCTs are important to numerous applications in science and engineering. I can perform **discrete** **cosine** **transformation** by different algorithms, in this paper we use pixels processing. In this paperI have implement method for image **transformation** and analysis using **discrete** **cosine** **transformation** technique and develop results using matlab coding. In future the MATLAB code can be converted to VHDL code and implement on FPGA kit inorder to develop ASIC (application specific IC) for image **transformation** and analysis. This will give a generalization to image processing.

Show more
Abstract-Steganography comes from the Greek words steganos, roughly translating as “covered writing”. Steganographic techniques allow one party to communicate information to another without a third party even knowing that the communication is occurred. The ways to deliver these “secret messages” is vary greatly. “The goal of steganography is to hide messages inside other harmless messages in a way that it does not allow any enemy to even detect that there is a second secret message present.” Steganography is a process that involves hiding a message in an appropriate carrier for example an image or an audio file. The carrier can then be sent to a receiver without anyone else knowing that it contains a hidden message. There are various technique being used in hiding the message using steganography practice, namely digital watermarking, least significant bit (LSB), fractal codes, masking and filtering, algorithms and transformations, **Discrete** wavelet **transformation**, **Discrete** **Cosine** **Transformation** and many more. This project will implement **Discrete** **Cosine** **Transformation** (DCT) as steganography technique in hiding text into an image. The process start with divides up the image into 8 by 8 pixel blocks, and then calculates the **discrete** **cosine** transform (DCT) of each block. A quantizer rounds off the DCT coefficients according to the quantization matrix. This step produces the "lossy" nature of Joint Photography Expert Group (JPEG) , but allows for large compression ratios. JPEG's compression technique uses a variable length code on these coefficients, and then writes the compressed data stream to an output file (.jpg). The purpose of this project is to develop a prototype software system that can hide a text into an image using **Discrete** **Cosine** **Transformation** (DCT) technique. It means that various algorithms and transformations are applied on the image to hide the text or messages. The system is build by using Matlab 7.6.

Show more
14 Read more

A color image normally is composed of same size R 、 G 、 B gray level images, and video image constitutes are multiple continuous static color images, so it can use four dimensional matrix model to express video image, four dimensional matrix **discrete** **cosine** **transformation**-based video compression algorithm can do without adopting motion compensation techniques among intra-frame coding.

The **discrete** **cosine** **transformation** (DCT) segments the image into spectral sub-bands of varying importance. Images can be transformed from the spatial domain to the frequency domain using the DCT technique. Cao et al. (2012) used a block-based method, the current **discrete** **cosine** **transformation** (DCT) was improved and the dimension of the feature vector was reduced thereby reducing the computational complexity. **Discrete** **cosine** transforms (DCT) and Gaussian RBF kernel PCA technique was proposed by Mahmood et al. (2016a) to solve the issue of increased dimensional nature of the feature space. Rather than extracting sparse features that give results that are less accurate, Bi et al. proposed a dense-field method based on DCT to extract features from the suspected image and afterwards, a new matching method based on the features extracted by colour texture descriptors and invariant moments descriptors is performed, and this solves the issue of high computational complexity resulting from using the dense-field approach. However, Ustubioglu et al. (2016), used DCT and element by element matching technique in the detection of copy-move forgery detection.

Show more
Abstract— Image fusion is a mixture of several images to a merged image ensuing more informative than other input images which have been used in recent years. Image fusion based on **Discrete** **Cosine** **Transformation** (DCT) is dealt in this work. Generally, in image fusion, several images of the same scene are given as input and one image with higher quality is obtained as output. When compared to Nyquist theorem, compressed sensing theory offers an improved result. DCT yields better quality and it requires less storage and low cost amongst the many techniques.

Abstract:- This Paper Focus On Image Compression.It is used specially for the compression of images where tolerable degradation is required. With the wide use of computers and consequently need for large scale storage and transmission of data, efficient ways of storing of data have become necessary. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages.JPEG and JPEG 2000 are two important techniques used for image compression.JPEG image compression standard use dct (**discrete** **cosine** transform). Now here Lapped Orthogonal Transform and **Discrete** **Cosine** **Transformation** are using with JPEG 2000 standard. The **discrete** **cosine** transform is a fast transform. It is a widely used and robust method for image compression. It has excellent compaction for highly correlated data.DCT has fixed basis images DCT gives good compromise between information packing ability and computational complexity

Show more
David Salomon [8] provides necessary consideration for image compression. An image compression method is specified for a specific type of images. New data compression methods that are developed and implemented have to be tested. Testing different methods on the same data makes it possible to compare their performance both in compression efficiency and speed. Hence, we have standard test data namely, ‗Lena‘, ‗Mandril‘ and ‗Peppers‘. They are continuous tone images although ‗Lena‘ image is mostly pure continuous tone, especially the wall and the bare skin areas. The hat is good continuous tone, where the hair and the plume on the hat are bad continuous tone. The straight lines on the wall & curved parts are features of **discrete** tone image. There are various approaches to compress the image. Various authors have discussed the different approaches with their relative advantage and disadvantage [1], [4], [5], [8], [9], [11], [15], [17], [21], [25], [27].

Show more
O’Ruanaidh et al. [8] have presented the theory of integral transform invariants and explained that this technique can be used to embed watermarks that are resistant to rotation, scaling, and translation. In their approach, the **discrete** Fourier transform (DFT) of an image is computed and then the Fourier-Mellin transform is performed on the magnitude, the watermark is embedded in the magnitude of the resulting transform. The watermarked image is reconstructed by performing the inverse transforms (an inverse DFT and an inverse Fourier-Mellin transform) after considering the original phase [9][8]. Fourier-Mellin transform is a log-polar mapping (LPM) followed by a Fourier transform, while an inverse Fourier-Mellin transform is an inverse log-polar mapping (ILPM) followed by an inverse Fourier transform. In the scheme, the embedded watermark may be extracted by transforming the watermarked image into RST invariant domain. This approach was effective in theory, but difficult to implement.

Show more
ECDSA (Elliptic Curve Digital Signature Algorithm) is the elliptic curve analog of well known digital signature crypto system. ECDSA is based on elliptic curve **discrete** logarithm problem (ECDLP) and ECDLP is significantly more difficult than IFP (Integer Factorization Problem) and DLP (**Discrete** Logarithm Problem) [9]. Therefore ECC (Elliptic Curve Cryptography) provides the highest strength- per-bit of any cryptosystem known today [10]. Therefore we use ECDSA in our definition to overcome the space limitation constrain.

The most important point in distinction of DCT and DFT that is, after implementation DFT, the result will be a complex function with amplitude and phase but for DCT, just a function with real coefficients is observable [1]. For these reasons and regarding to the variety of properties of the DCT **transformation** that it is useful tool in different applications of image processing, such as image compression in JPEG and etc.

Abstract. In this paper a computationally efficient and high- quality preserving DCT architecture is presented. It is ob- tained by optimizing the Loeffler DCT based on the Cordic algorithm. The computational complexity is reduced from 11 multiply and 29 add operations (Loeffler DCT) to 38 add and 16 shift operations (which is similar to the complexity of the binDCT). The experimental results show that the proposed DCT algorithm not only reduces the computational com- plexity significantly, but also retains the good **transformation** quality of the Loeffler DCT. Therefore, the proposed Cordic based Loeffler DCT is especially suited for low-power and high-quality CODECs in battery-based systems.

Show more
In recent years, several methods have been developed for hiding secret image into the cover image. Some method is developed in the time domain like least significant bit (LSB) substitution, phase coding steganography. LSB substitution is one of the earliest techniques used for the secret image data embedding in audio signals and other media types. The phase coding steganography methods are other time domain image steganography schemes which hide secret data in the phase of the cover image. Transform domain image steganography methods are the ones in which various transform domains such as **Discrete** Fourier **transformation** (DFT), **discrete** **cosine** **transformation** (DCT), and **Discrete** Wavelet **transformation** (DWT) are used to embed the secret image data in the coefficients of the cover image.

Show more
with accuracy from the training images even though some sort of distortion or deformation (up to certain extent) is present. **Discrete** Fourier **Transformation** representation allows manipulating the information content of the image globally by means of spatial manipulations of the transformed image. The DFT representation also encodes image information in a holistic distributed manner [18]. However due to the fact that **Discrete** **Cosine** **Transformation** representation tends to have more of its energy concentrated in a small number of coefficients compared to DFT, it is actively used in spectral analysis[13]. Fractional version of **cosine** **transformation** has close relationship with conventional **cosine** **transformation**. Fractional **cosine** **transformation** provides a tool to integrate the information of time domain and frequency domain at the same time [9].

Show more
This demand for fast and efficient coding algorithms in medical field has led to the development of several techniques which have revolutionized the area of image compression. This increase in the number of techniques has given rise to the dilemma of deciding which of these methods possess the best properties and potentials for effective coding. This problem is of particular importance in the medical field where the distortion of information may lead to inaccurate diagnosis. Thus, there exists a need to compare the different coding algorithms in order to see the advantages of each technique. This paper, in continuation with this thought, analyzes the suitability of lossy techniques for medical image compression. The research tries to answer the question “Which of the existing lossy techniques works better for medical images in terms of compression rate and quality?” and answering this question is the main goal of this work. The aim is to analyze techniques and find a compression scheme that can compress medical images quickly and at the same time reduce compression rate while maintaining a good level of visual quality. The various algorithms considered are Block Truncation Coding (BTC), **Discrete** **Cosine** **Transformation** (DCT), Embedded Zero-tree Wavelet (EZW) coding, Set Partition In Hierarchical Tree (SPIHT) Algorithm, Zero-Tree Entropy (ZTE) Coding Algorithm and Singular Value Decomposition (SVD).

Show more
Major radius and minor radius, respectively mm Computer aided design Charge coupled device Horizontal distance between dual sensors mm Discrete cosine transformation Depth of field µm Op[r]

15 Read more

It is a mathematical **transformation** which is related to the Fourier **transformation** which is similar to the **discrete** Fourier **transformation** but it uses only real numbers. . DCTs are equivalent to DFTs roughly it is twice the length and operating on real data with even symmetry, where in some variants the input and/or output data are shifted by half a sample. **Discrete** **Cosine** **Transformation** technique is often used in signal and image processing, mainly for lossy data compression, because it has a strong "energy compaction" property. DCTs are also widely employed in solving partial differential equations. Transform coding constitutes an integral component of contemporary image/video processing applications. It involves expression of data points in sequence in terms of **cosine** function’s sum oscillating at different frequencies. The use of **cosine** rather than sine functions is critical in these applications, for compression it turns out that **cosine** functions are much more efficient. Transform coding relies on the premise that pixels in an image exhibit a certain level of correlation with their neighbouring pixels. Similarly in a video transmission system, adjacent pixels in consecutive frames2 show very high correlation. Consequently, these correlations can be exploited to predict the value of a pixel from its respective neighbours. The transform coding comprises an important component of image processing

Show more
14 Read more

Watermarking methods are divided in two main categories called domain specific and the **transformation** specific. In case of spatial domain based approaches, the information message is directly embedded in the information object. The pixel replacement is here performed to achieve the watermarking. These kind of approaches includes the LSB(Least Significant Bit), Bit plane complexity based approach. LSB is considered as the most basic approach in which the last bit of each pixel is replaced to perform the watermarking. This kind of approach performs the pixel or the bit level substitution over the coer object. In case of bit plane complexity approach, the block level replacement is defined. In such approaches, the cover object is decomposed in smaller objects and the block extraction is performed over it. Once the blocks are obtained, the secret or hidden object blocks replaces this information object. DCT (**Discrete** **cosine** **Transformation**) and DWT (**Discrete** Wavelet **Transformation**) are these kind of watermarking approaches. These approaches performs the frequency or intensity level analysis over the cover objects to identify the effective area in which the information hiding will be performed.

Show more
Abstract— Image compression technique is a request of data compression on digital images which encodes the original image with few bits. The intended of image compression is used to minimizing the redundancy of the picture and to keep or broadcast information in an efficient manner. It is carried out the problem of minimizing the huge data needed to characterize a digital image. In PDF application, extraction of images becomes more highly required to analyze the image content. But, the Lossless Compression is insufficient to measure the correlation amid the images and the DCT is not efficiently considered. The sparse representation of double **discrete** wavelet transform (DDWT) is a generative method of image blur that determines wavelet analysis of a blurred image. The DDWT offers less reliable for identifying object motion blur. Then, presented an image decomposition model is used to reduce the noise in the images but is less optimization provided. Then, High quality, noise removed image contents are stored in image file and subjected for image compression and decompression in PDCT-LIC technique. Finally, Polynomial **Discrete** **Cosine** **Transformation** Lossless Image Compression (PDCT-LIC) technique is developed to improve compression ratio, quicker compression/ decompression time, reduce compressed file size and minimize information loss.

Show more
For **Discrete** **Cosine** **Transformation** the functions of the oscillation coefficient in various frequencies reveal a limited sequence of data points in total. **Discrete** **Cosine** **Transformation** works to highlight images within the areas of different frequencies. During the quantisation, the abstraction part is actually present lower mainstream frequencies, only the most important frequencies are preserved and retrieve to the original image. As a result, some of the reconstructed pictures have been distorted. When the compression level is adjusted, quality of image can be adjusted. But the human eye can only identify if the distortion reaches to a certain level.

Show more