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Digital video compression techniques have a crucial contribution in the world of telecommunication and multimedia sector where bandwidth is a valued constraint. A large amount of multimedia data has to be stored in a limited storage space. Hence, video compression techniques mainly focus on reducing the volume of information required for picture sequences/streaming pictures without losing much of its quality. Thus, in order to provide an competent compression method for multimedia data, Luminance Masking technique was proposed. In this context, an Intensity Dependant Spatial Quantization (IDSQ) perceptual means is proposed which attempts the intensity masking of the human visual system and perceptually adjusts quantization. IDSQ allows for adaptation to the video characteristics and its design meets low complication implementation requirements The proposed method has been incorporated into the HEVC reference model for the HEVC Range Extensions and its performance was judged by measuring the bit rate reduction against the HEVC Range Extensions.

Image compression is an implementation of the data compression which encodes actual image with some bits. Thepurpose of the image compression is to decrease the redundancy and irrelevance of image data to be capable to record or send data in an effective form. Hence the image compression decreases the time of transmit in the network and raises the transmission speed. In Lossless technique of image compression, no data get lost while doing the compression. To solve these types of issues various techniques for the image compression are used. Now questions like how to do mage compression and second one is which types of technology is used, may be arises. For this reason commonly two types’ of approaches are explained called as lossless and the lossy image compression approaches. These techniques are easy in their applications and consume very little memory. An algorithm has also been introduced and applied to compress images and to decompress them back, by using the Huffman encoding techniques.

Data mining is the technique by which one can extract efficient and effective data from huge amount of raw data. There are various techniques for extracting useful data. Attribute Selection is one of the effective methods for this operation. To obtain useful data from enormous amount of data is not a simple task. It contains several phases like preprocessing, classification, analysis etc. Attribute Selection is an important topic in Data Mining, as it is the effective way to reduce dimensionality, to remove irrelevant data, to remove redundant data, & to increase accuracy of the data. It is the process of identifying a subset of the most useful attributes that produces attuned results as the original entire set of attribute. In last few years, there were different techniques for attribute selection. These techniques were judged on two measures i.e. efficiency is time to find the clusters and effectiveness is quality of subset attributes. Some of the techniques are Wrapper approach, Filter Approach, Relief Algorithm, Distributional clustering etc. But each of one having some drawbacks like unable to handle large volumes of data, computational complexity, accuracy is not guaranteed, difficult to evaluate and redundancy detection etc. To overcome some of these problems in attribute selection method this paper proposes technique that aims to provide an effective clustering based attribute selection method for high dimensional data. Initially this technique removes irrelevant attributes depending on some threshold value. Afterwards, using Kruskal’s algorithm minimum spanning tree is constructed from these attributes. From that tree some representative attributes are selected by partitioning. This is nothing but the final set of attributes.

Steganography is the technique of hiding data in an appropriate multimedia carrier, e.g., image, audio, and video files known as Cover. Images are mostly used as the cover medium due to their pervasiveness in different applications and representation with high redundancy. This paper provides a review and analysis of many existing methods for digital image steganography in the spatial as well as transform domain. The performance evaluation of the algorithms with respect to the proposed analysis parameters are summarized along with their limitations inorder to throw some light on the utility of the algorithm as per the requirement of application. General Terms Steganography

In this paper, we present a demonstration for the urban images to prove the existence of the redundancy of information on three spectral bands. The thing that we can notice it visually and proven thanks to a statistical study carried out on the urban image. The two treatments (visual, statistical) assure us that the images of the road have a strong correlation

pyramid provides multi-resolutional format that mirrors multiple scales of processing in the human visual system. Pyramid construction tends to enhance image features, such as edges, which are important for interpretation. Gaussian pyramid techniques are efficiently used for multi-scale edge estimation, to compute coarse scale images and also for finer details of the images .Laplacian pyramids methodology help in efficient compression and redundancy removal. The methodologies that are into picture helps to develop filter- based representations in order to decompose images into information at multiple scales, to extract features of interest and also in attenuating noise.

Image compression plays vital role in the field of the corporate world, entertainment industry, multimedia, education communications and even at home. The main objective of Image compression is to minimize the size of image in bytes by reducing the redundancy of the image data without degrading the quality of image that results in the reduction of file size so that more images can be stored in a given amount of disk or memory space and also reduces the time required to send the images over the network. Inspired by the recent advancements in the image compression techniques, we proposed an image compression framework based on the pixel-wise fidelity. The proposed method is used to reduce the bit rate of the pixels of the image for the compression by using angular transformation. In the proposed algorithm we used the normalization technique and the inverse sine transformation to get the optimized and compressed image as an output. Finally the results are compared with different methods for MSE, PSNR and the visual appearance after decoding of image.

To reduce the redundancy of image, image compression is used in order to store data in much more effective way. For effective & efficient image compression integer wavelet transform has been introduced. The aim of the integer wavelet transform is to get maximum compression ratio possible. Decomposition of image takes place up to certain levels; at different levels the value of image compression is different. Maximum compression & minimum compression can be achieved by integer wavelet transform method.

Arithmetic coding is used in many compression techniques during the entropy encoding stage. Further compression is not possible without changing the data model and increasing redundancy in the data set. To increase the redundancy, we have applied index based byte-pair transformation (BPT-I) as a pre-processing to arithmetic coding. BPT-I transforms most frequent byte-pairs (2-byte integers). Here, most frequent byte-pairs are sorted in the order of their frequency and groups consisting of 256 byte-pairs are formed. Each bytepair in a group is then encoded using two tokens: group number and the location in a group. Group number is denoted using variable length prefix codeword; whereas location within a group is denoted using 8-bit index. BPT-I is designed to be applied on any type of source; not necessarily text. More the number of groups considered during transformation, better is the compression. Experimental results have shown around 4.30% additional reduction in compressed file size when arithmetic coding is applied after byte-pair data transformation BPT-I. General Terms Data Compression, Algorithms

The Feature Selection is one of the key processes for knowledge acquisition. Some data set is multidimensional and larger in size. When this data set is used for classification it may end with wrong results and it may also occupy more resources especially in terms of time. Most of the features present are redundant and inconsistent and affect the classification. In order to improve the efficiency of classification these redundancy and inconsistency features must be eliminated. The Feature subset contains the minimum number of features that most contribute to accuracy In this paper, present a method for dealing with feature subset selection based on fuzzy Information measures for handling classification problems. First, to construct the membership function of each fuzzy set of a feature. Then, select the feature subset based on the proposed fuzzy Informationy measure focusing on boundary samples. It also presents an experiment result to show the applicability of the proposed method. The performance of the system is evaluated in MATLAB on several benchmark data sets in the UCI machine learning repository.

DCT based JPEG compression is a widely used standard for lossy image compression. DCT concentrates energy into lower order coefficients. It removes redundancy between neighbouring pixels which leads to uncorrelated transform coefficients which can be encoded independently. There is high computational complexity in DCT. DHT is a real valued transform whose forward and inverse transforms are same except for a an inclusion of a scale factor in the inverse transform. DHT reduces the computational complexity of JPEG compared to DCT. DHT doesn’t require a dequantizer at the decoder, since a new quantization technique known as energy quantization is used. It speeds up the encoding procedure, reduces hardware as well as makes the implementation simpler. The quality of the reconstructed image is very good which is verified using MATLAB i.e. the PSNR is improved and the MSE is reduced.

rganizations such as IT industry, colleges and Scientists regularly encounter problems to handle large data sets for their different purpose in many areas as for example biological research. These limitations also affect internet search to fetch data, business for analysis etc. So it is simply needed generalized but special types of compression algorithm for dissimilar data to get utmost saving percentage. In this article Compression of biological data that is single and double strand DNA and single strand RNA have been considered. Since biological data are less random compare to any text data that means redundancy within the sequences are more but they have some special property as for example different types of repeat one of such repeat is called dinucleotide repeat .This type of repeat are more in any sequence. Here the two proposed algorithm are based on this repeat using static fixed length LUT for input file and output file mapping.

compression is to reduce irrelevance data and redundancy of the image data in order to be able to store or transmit data in an efficient form. Image compression scheme either be in lossy method or lossless method. Lossy algorithms are especially suitable for transmit images across the network with minor (sometimes imperceptible) loss of fidelity of information. In this work, propose MPC (Maximum PSNR using Coefficient) using DCT algorithm for image compression and reconstruction taking benefit from the advantages of DCT algorithms. The algorithm performs the Discrete Cosine Transform (DCT) on the coefficients. The objective of the proposed system to study how the image is compressed using MPC method and discrete cosine transforms to attain maximum PSNR value. To attain maximum PSNR value, using coefficient, the images are analyzed and get compressed image with good quality. Coefficient reads the index of RGB values of given input image and processing that image in iteration manner. Coefficient is the most important factor that affects the image quality. Using Coefficients in Matlab, the images are analyzed and get maximum PSNR value is attained.

Data compression is used for reducing storage requirements. It involves transforming data of a given format, called source message, to data of a smaller sized format, called compressed message. The Data compression helps in reducing the size of the database so that it is very easy to maintain huge database. On the other hand Type Casting is used to convert the input source message into suitable format so that Huffman Algorithm can work on maximum data formats and after that Data Normalization technique is used to remove redundancy from the data this technique enhance the compression efficiency of Huffman Algorithm. Decreasing the amount of data required representing a source of information while preserving the original content as much as possible and after that providing security to this compressed code word with the application of data encryption. The main objectives of this paper are to get higher compression efficiency by applying a planned technique on Huffman Algorithm and providing security to this compressed data to limit the unauthorized access using data encryption.

paper, we present an new approach to face recognition based on the combination of feature extraction methods, such as two-dimensional DWT-2DPCA and DWT-2DLDA, with a probabilistic neural networks. This later is used to classify the features matrix extracts for space data created by Two- dimensional Subspace Analysis .The technique 2D-DWT is used to eliminate the illumination ,noise and redundancy of face in order to reduce calculations of the probabilistic neural network operations ,and improve a face recognition system in accuracy and computation time. The proposed approach is tested on ORL and FEI face databases. Experimental results on this databases demonstrated the effectiveness of the proposed approach for face recognition with high accuracy compared with previous methods..

In data mining, association rule mining is very strong and limited by the huge amount of delivered rules, because of these so many problems facing to implementation. To overcome these drawbacks, several methods were proposed in the literature such as item sets concise, redundancy reduction, and post processing. Based on statistical information by using these methods the extracted rules may not be useful for the user. Thus, it is crucial to help the decision-controller with an efficient post processing step in order to reduce the number of rules. In this paper we have implemented a new interactive approach using cost complexity pruning and filter discovered rules, by using this approach we have to reduce the tree size with minimum number of error of validation set.

This paper presents an efficient ECG signals compression techniques using a 2D DWT coefficient thresholding and its design implementation of an efficient JPEG2000 encoder that employs the Distributed Arithmetic (DA) technique for the complex computation of Discrete Wavelet Transform (DWT).2D approaches exploit the fact that redundancy of ECG signal occurs between adjacent beats and adjacent samples. Cut and aligned error signal to form 2D matrix, then 2D matrix is transformed. Ultimately wavelet coefficients are segmented into groups and thresholded. DA-DWT is used for reducing the complex computations, which can increase the speed and throughput as well. Architecture is based on the principles of pipelining and parallelism to obtain the optimal speed and throughput. Architecture is simple, modular and cascadable for computing a DA-DWT. This technique is faster when ROM table in on chip memory and memory size is reduced by splitting ROM table .The description and functionalities of the design is modeled by Verilog HDL. The simulation and synthesis methodology are used to target it on Virtex-II Pro FPGA (xc4v1x25-12sf363) that consumes 6% resources of FPGA and shows the clock frequency 310.207 MHz by DADWT using Sym20. Experiments on selected records from MIT-BIH arrhythmia database revealed that the proposed design is significantly more efficient.

Video Compression has played an important role in Multimedia data storage and transmission. Video compression techniques removes spatial as well as temporal redundancy using intra-frame and inter-frame coding respectively. A large level of compression can be achieved through inter-frame coding. In this paper, a new matching criteria is proposed for the video compression using block matching techniques. Along with this, performance of four matching criterion in the temporal coding of video signal, which are Minimum Mean Absolute Error, Vector Matching Criterion, Smooth Constrained - Mean Absolute Error, and proposed algorithm using new pixel values are compared. Three step search algorithm has been used for searching the matching block as a block matching technique, because three step search algorithm is very simple and efficient search algorithm, which provides near optimum results only in three steps. For the various videos it has been experimentally observed that the minimum average error per pixel, and minimum search points per block reduces upto 0 per pixel and 13.31 per block respectively using proposed criteria and the values for the same parameters and same set of frames are very high using other criterion.

A key benefit of channel coding is the ability to protect data against channel impairments. Essentially, the channel coder adds redundancy bits that provide a way to correct corrupted data. Encryption is another way of protecting data against channel impairments and unintended interception or forging. This paper introduces an efficient approach of increasing coding gain at the receiver end. The implemented approach uses the advanced encryption standard (AES) cryptographic algorithm as a primary part of channel coding process. The simulation result of this implemented approach is evaluated in the presence of additive white Gaussian noise (AWGN).

Now a days, large files such as digital images, to be easily transmitted over the internet. New problems associated with this are security and privacy of image. Using pseudorandom permutation, image encryption is obtained. Confidentiality and access control is done by encryption. Encrypted image is compressed by using orthogonal transform. Irrelevance and redundancy of the image data is reduced by image compression because this is easy to store or transmit data in an efficient form. Reconstruct the original image by iterative reconstruction procedure. In this, flexible compression ratio is used and significantly improves the compression efficiency.

Communication between a sender and receiver needs security. It can be done in any form, like plain text or binary data. Changing the information to some unidentifiable form, can save it from assailants. For example, plain text can be coded using schemes so that a stranger cannot apprehend it. Cryptography is a subject or field which deals with the secret transmission of messages/ data between two parties. Cryptography is the practice and study of hiding information. It enables you to store delicate information and transmit it across insecure networks so that it cannot be read by anyone except the authorized recipient. Applications of cryptography include ATM cards, computer passwords, and electronic commerce etc. Messages, which are being sent between two parties, should be of small size so that they occupy less space. Data compression involves encoding of information using fewer bits than the original representation. Compression algorithms reduce the redundancy in data representation to decrease the storage required for that data. Symmetric key cryptography algorithms [6] are fast and mostly used type of encryption. Many types of data compression techniques do exist. Huffman Compression [5] is a lossless data compression technique and can be considered as the most efficient algorithm. In this paper, a combination of new Symmetric key algorithm and existing Huffman compression algorithm has been proposed. Proposed method works on text data. Algorithms have been provided in the paper itself. General Terms Algorithms, Compression, Security.

Compression reduces redundancy in data representation in order to achieve saving in the cost of storage and transmission. Image compression compensates for the limited on-board resources, in terms of mass memory and downlink bandwidth and thus it provides a solution to the "bandwidth vs. data volume" dilemma of modern spacecraft. Thus compression is very important feature in payload image processing units of many satellites. A low complexity and high efficiency near-lossless image compression algorithm is suggested in this paper. The algorithm provides the average compression ratio of 1.403 with high image quality for lossless compression. Compression ratio increases as ∆ parameter increases. Using proposed algorithm compression ratio of 4.208 is achieved for near-lossless compression. The proposed algorithm has low memory cost suitable for hardware implementation.

recognition has been studied for many years in the context of biometrics and is one of the most successful applications of image analysis and understanding. Various methods, approaches and algorithms for recognition of human faces were proposed. In this paper, independent, comparative study of conventional discrete wavelet transform (DWT), real dual-tree discrete wavelet transform (R-DT-DWT), and complex dual-tree discrete wavelet transform (C-DT-DWT) based features for face recognition is carried out. In 2005, Delac et al. (26) presented an independent comparative study of PCA, ICA, and LDA on the FERET data set where it was concluded that no particular distance-metric combination is the best. In this paper we intend to bring further conclusions. Unlike the contribution by Delac et al., our conclusions are in context of DWT, R-DT-DWT, and C-DT-DWT. Moreover, these approaches are tested on nine different databases at different levels and under three different distance metrics, which allowed us to compare their performance independently. Our simulation results show that no particular distance-metric combination is the best across all standard benchmark face databases. However, the overall performance for city block distance measure was found to be better as compared to the Euclidean and cosine distance. Also, the performance for R-DT-DWT and C-DT-DWT based features were found equivalently efficient in many cases. So taking redundancy into consideration, it may be suggested to opt for R-DT-DWT for face efficient recognition.

Steganography can be defined as a method of hiding data within a cover media so that other individuals fail to realize their existence. Image, audio and video are some popular media for steganography. But text is ideal for steganography due to its ubiquity and smaller size compared to these media. However, text communication channels do not necessarily provide sufficient redundancy for covert communication. In this paper, a new approach for steganography in cricket match scorecard is proposed. The main idea is that additional zeroes can be added before a number without changing the value of a number in a scorecard. General Terms Steganography, Information Security

To propose a new text compression technique for ASCII texts for the purpose of obtaining good performance on various document sizes. This algorithm is composed of two stages. In the first stage, the input strings are converted into the dictionary based compression. In the second stage, the redundancy of the dictionary based compression is reduced by Burrows wheeler transforms and Run length coding. The algorithm has good compression ratio and reduces bit rate to execute the text with increase in the speed. General Terms Lossless text compression, Dictionary making algorithm

This paper represents lossy compression technique which can be effectively used for correlated pixels in both continuous and discontinuous series. Generally, compression is achieved by reducing redundancy through removal of similar pixels. Here, dependency of effective compression over their continuity has been removed. The efficiency of the proposed method has been described mathematically, proving it suitable for higher bit images.