Image compression

Transform coding has been the focus of the research for image compression. In previous research, the Amplitude and Group Partitioning (AGP) coding scheme is proved to be a low complexity algorithm with high performance 1,2 , clearly one of the state-of-art transform coding techniques. However, the previous AGP is used along with the Discrete Cosine Transform (DCT) and the discrete wavelet transform. In this paper, a different transform, the Lapped Orthogonal Transform (LOT), replaces the DCT in conjunction with the AGP. This is the first time LOT and AGP have been combined in a coding method. The definition and design of the LOT are discussed. An objective metric to measure the performance of transform, coding gain, is calculated for both the DCT and the LOT. The LOT has slightly higher coding gain than the DCT. The principles of the LOT based AGP image codec (LOT-AGP) are presented and a complete codec, encoder and decoder, is implemented in software. The performance of the LOT-AGP is compared with other block transform coding schemes: the baseline JPEG codec 4 and the DCT based AGP image codec 1 (DCT-AGP) by both objective evaluation and subjective evaluation. The Peak Signal to Noise Ratio (PSNR) is calculated for these three coding schemes. The two AGP codecs are much better than the JPEG codec on PSNR, from about 1.7dB to 3 dB depending on bit rate. The two AGP schemes have PSNR differences only to a small degree. Visually, the LOT-AGP has the best-reconstructed images among these three at all bit rates. In addition, the coding results of two other state-of-art progressive image codecs are cited for further comparison. One is the Set Partitioning in Hierarchical Trees (SPIHT) algorithm 5 with a dyadic wavelet transform, and the other is Tran and Nguyen's method with the generalized LOT transform 6 . The AGP coding and the adaptive Huffman entropy coding of LOT-AGP are less complex and the memory usage is smaller than in these two progressive codecs. Comparing these three codecs, i.e. the LOT-AGP and the two progressive codecs in PSNR small only small differences in PSNR. SPIHT has about 1 dB higher PSNR than the LOT-AGP and Tran and Nguyen's method for the test image Lena. For the test image Barbara, the PSNR of the LOT-AGP is about 0.5 dB higher than that of the SPIHT and 0.5 dB lower than that of Tran and Nguyen's method. This low-complexity and high performance codec may provide a new direction for the implementation of image compression.

Bu makalede imge sıkıştırma uygulamalarında kullanılmak uzere yeni bir renk dönüşümü metoduönerilmektedir. Bahsi geçen renk dönüşümünün katsayıları, imgenin renk histogramı kullanılarak hesaplanmakta ve bu sayede imgeyë ozel bir renk dönüşümü elde edilmektedir.Önerilen yeni dönüşümün sıkıştırma başarısı JPEG algoritması kullanılarak gösterilmiştir. Literatürde yapılan benzer testlerde sıkça kullanılan 15 resimüzerinde yapılan deneylerde algoritmanın farklı sıkıştırma oranlarına karşılık gelen PSNR degerlerine bakıldıgında, JPEG algoritmasına oranla daha iyi sonuçlar verdigi gözlenmiştir.

In Image Compression, the researchers' aim is to reduce the number of bits required to represent an image by removing the spatial and spectral redundancies. Recently discrete wavelet transform and wavelet packet has emerged as popular techniques for image compression. The wavelet transform is one of the major processing components of image compression. The result of the compression changes as per the basis and tap of the wavelet used. It is proposed that proper selection of mother wavelet on the basis of nature of images, improve the quality as well as compression ratio remarkably. We suggest the novel technique, which is based on wavelet packet best tree based on Threshold Entropy with enhanced run-length encoding. This method reduces the time complexity of wavelet packets decomposition as complete tree is not decomposed. Our algorithm selects the sub-bands, which include significant information based on threshold entropy. The enhanced run length encoding technique is suggested provides better results than RLE. The result when compared with JPEG-2000 proves to be better.

Transmission of compressed medical images is becoming a vital tool in telemedicine. Thus new methods are needed for efficient image compression. This study discovers the best design parameters for a data compression scheme applied to digital magnetic resonance (MR) images. The proposed technique aims at reducing the transmission cost while preserving the diagnostic information. By selecting the wavelet packet's filters, decomposition level, and subbands that are better adapted to the frequency characteristics of the image, one may achieve better image representation in the sense of lower entropy or minimal distortion. Experimental results show that the selection of the best parameters has a dramatic effect on the data compression rate of MR images. In all cases, decomposition at three or four levels with the Coiflet 5 wavelet (Coif 5) results in better compression performance than the other wavelets. Image resolution is found to have a remarkable effect on the compression rate.

This paper investigates the application of a new concept called the "Overcomplete Discrete Wavelet Transform"(0DWT) for image sequences compression. It has been demonstrated that: "The translated function with any integer multiple of the sampling period is completely represented in the wavelet space by one of the ODWT members". This theoretical result leads to a new motion estimation and motion compensation scheme working in the wavelet transform domain. Our simulation experiments, performed on real image sequences, show high quality and low bit rate performances. By performing the motion estimation in the wavelet space, a quite modest computational complexity is ensured.

We study the compression performance of the quincunx discrete wavelet transform (DWT) and we compare it with the dyadic DWT in terms of PSNR and bit rate. The 2D non-separable quincunx wavelet filters are designed by making use of the 'transformations of variables' technique of Tay & Kingsbury starting from the 1D biorthogonal (9,7)-taps filters. The applied transformation functions are respectively based on the Kaiser and Chebyshev windows, and on the Lagrange halfband filters. The novelties of this work are in the evaluation of these filters for image compression and the usage of an embedded coder based on the quadtree approach for coding the quincunx subbands.

Image compression is a very important issue for several applications in the area of multimedia communications, the objective being reduction of storage and transmission costs. Many efficient coding techniques have been developed for various applications. Amongst them, the Joint Photographic Experts Group (JPEG) has been recommended for compression of continuous tone still images. However, the reconstructed images from JPEG compression produce annoying blocking artifacts near block boundaries, particularly in highly compressed images, because each block is transformed and quantized independently. Several techniques/algorithms have been proposed by researchers, both in spatial and frequency domains, for reduction of these artifacts with varied degree of success. These are briefly overviewed here. A new technique working in frequency domain, is proposed here by authors. This paper puts forth a method and an algorithm, working in frequency domain, for the detection and reduction of such blocking artifacts. These artifacts are modeled here as 2-D step functions between two neighboring blocks. Presence of the blocking artifacts is detected by using block activity based on human visual system (HVS) and block statistics. The boundary regions between blocks are identified as either smooth or non-smooth regions. The blocking artifacts in smooth regions are removed by modifying a few DCT coefficients appropriately, whilst an edge-preserving smoothing filter is applied to the non-smooth regions, i.e., genuine edges. The algorithm has been applied to variety of JPEG compressed images and results are compared with other postprocessing algorithms. The reduction in the blocking artifacts for each image have been evaluated using three indices, namely peak signal-to-noise ratio (PSNR), mean structure similarity (MSSIM) index based on human visual perception, and a new index, called here block boundary measure (BBM), applied

This paper presents an ECG compressor based on optimized quantization of Discrete Cosine Transform (DCT) coefficients. The ECG to be compressed is partitioned in blocks of fixed size, and each DCT block is quantized using a quantization vector and a threshold vector that are specifically defined for each signal. These vectors are defined, via Lagrange multipliers, so that the estimated entropy is minimized for a given distortion in the reconstructed signal. The optimization method presented in this paper is an adaptation for ECG of a technique previously used for image compression. In the last step of the compressor here proposed, the quantized coefficients are coded by an arithmetic coder. The Percent Root-Mean-Square Difference (PRD) was adopted as a measure of the distortion introduced by the compressor. To assess the performance of the proposed compressor, 2-minute sections of all 96 records of the MIT-BIH Arrhythmia Database were compressed at different PRD values, and the corresponding compression ratios were computed. We also present traces of test signals before and after the compression/decompression process. The results show that the proposed method achieves good compression ratios (CR) with excellent reconstruction quality. An average CR of 9.3:1 is achieved for PRD equal to 2.5%. Experiments with ECG records used in other results from the literature revealed that the proposed method compares favorably with various classical and state-of-the-art ECG compressors. 

This paper addresses an image prediction problem focused on images with no identifiable objects. In it, we present several approaches to predict the next image of a given sequence, when the image lacks the well-defined objects, such as meteorological maps or satellite imagery. In these images no clear borders are present, and any object candidate moves, changes, appears and disappears in any image. Nevertheless, this evolution, though unrestricted, is gradual and, hence, prediction looks feasible. One of the approaches presented here, based on a spatio-temporal autoregressive (STAR) model, offers good results for these kinds of images. The main contribution of this paper is to adapt spatio-temporal models to an image prediction problem. As a byproduct of this research, we have achieved a new image compression method, suitable for images without defined shapes.

Digital images in their uncompressed form require an enormous amount of storage capacity which in turn needs large transmission bandwidth for the transmission over the network. Image compression reduces the storage space of image and also maintains the quality of image. There are various compression techniques available in literature. Discrete Cosine Transform (DCT) is one of the widely used image compression method and the Discrete Wavelet Transform (DWT) is another which provides improvements in the quality of the picture. This research paper proposed a scheme for image compression using DCT and DWT named as hybrid compression technique. DCT has high energy compaction property and often require less computational resources and DWT is multi-resolution transformation. The goal is to achieve higher compression rates with preserving the quality of the reconstructed image.

Digital radiology places very high demands on the networking and digital storage infrastructure of hospitals. In addition to having quite stringent requirements on the quality of the images displayed to the radiologist, much of the technical challenge resides in the necessity of displaying desired images as rapidly as possible. We present an infrastructure for progressive transmission and compression of medical images, which can refine an initial image by increasing the detail information not only in scale-space, but also in coefficient precision. The approach is based on the embedded zerotree wavelet (EZW) algorithm. This algorithm offers a tremendous amount of flexibility in meeting the bandwidth and image quality constraints in a radiological imaging environment. We propose an application of the EZW algorithm in progressive medical image transmission in which it can specify and control both the resolution constraint and rate constraint. The presented method can provide a framework for lossy image compression, with performance far superior to those provided by the standard JPEG algorithm. Also due to the flexibility of the method we will show how any region of interest of an image can be sent progressively.

JPEG2000 is an international standard for still images intended to overcome the shortcomings of the existing JPEG standard. Compared to JPEG image compression techniques, JPEG2000 standard has not only better not only has better compression ratios, but it also offers some exciting features. As it's hard to meet the real-time requirement of image compression systems by software, it is necessary to implement compression system by hardware. The MQ decoder of the JPEG2000 standard is an important bottleneck for real-time applications. In order to meet the real-time requirement we propose in this paper a novel architecture for a MQ decoder with high throughput which is comparable to that of other architectures and suitable for chip implementation. This architecture has been implemented in VHDL hardware description language and synthesized using Xilinx's and Altera's design flows respectively ISE 13.1 and Quartus. The implementation results show that the design operates at 439.5 MHz when implemented on Virtex-6 and the estimated frame rate at this frequency is 63.24 frames per second (FPS). On Stratix III device, the design operates at 214.4 MHz and the hardware cost is very low. Hardware overhead is minimized to a great extent because the structure of the probability estimation table (PET) is replaced by a small PET ROM. The memory bits used in the architecture are reduced significantly. The use of a dedicated probability estimation table decreases the internal memory.

A new image compression algorithm is proposed, based on independent Embedded Block Coding with Optimized Truncation of the embedded bit-streams (EBCOT). The algorithm exhibits state-of-the-art compression performance while producing a bit-stream with a rich set of features, including resolution and SNR scalability together with a "random access" property. The algorithm has modest complexity and is suitable for applications involving remote browsing of large compressed images. The algorithm lends itself to explicit optimization with respect to MSE as well as more realistic psychovisual metrics, capable of modeling the spatially varying visual masking phenomenon.

We consider the problem of simultaneous bitrate allocation for multiple video streams. Current methods for multiplexing video streams often rely on identifying the relative complexity of the video streams to improve the combined overall quality. In such methods, not all the videos benefit from the multiplexing process. Typically, the quality of high motion videos is improved at the expense of a reduction in the quality of low motion videos. In our approach, we use a competitive equilibrium allocation of bitrate to simultaneously improve the quality of all the video streams by finding trades between videos across time. A central controller collects rate-distortion information from each video user and makes a joint bitrate allocation decision. Each user encodes and transmits his video at the allocated bitrate through a shared channel. The proposed method uses information about not only the differing complexity of the video streams at every moment but also the differing complexity of each stream over time. Using the competitive equilibrium bitrate allocation approach for multiple video streams, simulation results show that all the video streams perform better or at least as well as with individual encoding. The results of this research will be useful both for ad hoc networks that employ a cluster head model and for cellular architectures.

This paper addresses the problem of wireless transmission of a captured scene from multiple cameras, which do not communicate among each other, to a joint decoder. Correlation among different camera views calls for distributed source coding for efficient multiview image compression. The fact that cameras are placed within a short range of each other results in a high level of interference, multipath fading, and noise effects during communications. We develop a novel two-camera system, that employs multiterminal source coding and complete complementary data spreading, so that while the former technique exploits the statistical correlation between camera views, and performs joint compression to reduce transmission rates, the spreading technique will protect transmitted data by mitigating the effects of wireless fading channels. Our results indicate that the proposed system is competitive when compared to two independently JPEG encoded streams at low to medium transmission rates.

Image compression is a widely addressed researched area. Many compression standards are in place. But still here there is a scope for high compression with quality reconstruction. The JPEG standard makes use of Discrete Cosine Transform (DCT) for compression. The introduction of the wavelets gave a different dimensions to the compression. This paper aims at the analysis of compression using DCT and Wavelet transform by selecting proper threshold method, better result for PSNR have been obtained. Extensive experimentation has been carried out to arrive at the conclusion.

The Singular Value Decomposition expresses image data in terms of number of eigen vectors depending upon the dimension of an image. The psycho visual redundancies in an image are used for compression. Thus an image can be compressed without affecting the image quality. This paper presents one such image compression technique called as SVD. Basic mathematics of SVD is dealt with in detail and results of applying SVD on an image are also discussed. The MSE and compression ratio are used as thresholding, parameters for reconstruction. SVD is applied on variety of images for experimentation. The work is concentrated to reduce the number of eigen values required to reconstruct an image.

In the recent years, Internet multimedia applications have become very popular. Valuable multimedia content such as digital images, however, is vulnerable to unauthorized access while in storage and during transmission over a network. Streaming digital images also require high network bandwidth for transmission. For effective image transmission over Internet; therefore, both security and bandwidth issues must be considered. In this paper, we present a novel scheme, which combines Discrete Wavelet Transform (DWT) for image compression and block cipher Data Encryption Standard (DES) for image encryption. The simulation results indicate thaf our proposed method enhances the security for image transmission over the Internet as well as improves the transmission rate.

DjVu is an image compression technique specifically geared towards the compression of scanned documents in color at high resolution. Typical magazine pages in color scanned at 300dpi are compressed to between 40 and 80 KB, or 5 to 10 times smaller than with JPEG for a similar level of subjective quality. The foreground layer, which contains the text and drawings and requires high spatial resolution, is separated from the background layer, which contains pictures and backgrounds and requires less resolution. The foreground is compressed with a bi-tonal image compression technique that takes advantage of character shape similarities. The background is compressed with a new progressive, wavelet-based compression method. A real-time, memory efficient version of the decoder is available as a plug-in for popular web browsers.

The Joint Photographic Experts Group (JPEG) committee is a joint working group of the International Standardization Organization (ISO) and the International Electrotechnical Commission (IEC). The word "Joint" in JPEG however does not refer to the joint efforts of ISO and IEC, but to the fact that the JPEG activities are the result of an additional collaboration with the International Telecommunication Union (ITU). Inspired by technology and market evolutions, i.e. the advent of wavelet technology and need for additional functionality such as scalability, the JPEG committee launched in 1997 a new standardization process that would result in 2000 in a new standard: JPEG 2000. JPEG 2000 is a collection of standard parts, which together shape the complete toolset. Currently, the JPEG 2000 standard is composed out of 13 parts. In this paper, we review these parts and additionally address recent standardization initiatives within the JPEG committee such as JPSearch, JPEG-XR and AIC.

The field of CCTV surveillance is topical and widely used in many different applications. The fundamental part of the CCTV system is a reliable image evaluation by a human observer, whose effectiveness is influenced by many variables. Optimization of the effectiveness is a multidimensional problem related to both technical and human characteristics. In many applied systems, the overall performance is affected by a real performance of technical system (image compression, channel transmission, etc.). On the other hand these technical systems have different optimization criteria than a typical video system. TNO Defence, Security and Safety (formerly the TNO Physics and Electronics Laboratory) initiated a project in which the influence of several technical variables on the operator's effectiveness was studied. The whole project was carried out in close co-operation with the Czech Technical University in Prague (CTU). The paper demonstrates and summarizes selected experimental results and evaluated dependencies.

A practical impossibility of prediction of signs of DCT coefficients is generally accepted. Therefore each coded sign of DCT coefficients occupies usually 1 bit of memory in compressed data. At the same time data of all coded signs of DCT coefficients occupy about 20-25% of a compressed image. In this work we propose an effective approach to predict signs of DCT coefficients in block based image compression. For that, values of pixels of already coded/decoded neighbor blocks of the image are used. The approach consist two stages. At first, values of pixels of a row and a column which both are the nearest to already coded neighbor blocks are predicted by a context-based adaptive predictor. At second stage, these row and column are used for prediction of the signs of the DCT coefficients. Depending on complexity of an image proposed method allows to compress signs of DCT coefficients to 60-85% from their original size. It corresponds to increase of compression ratio of the entire image by 3-9% (or up to 0.5 dB improvement in PSNR).

The JPEG baseline standard for image compression employs a block Discrete Cosine Transform (DCT) and uniform quantization. For a monochrome image, a single quantization matrix is allowed, while for a color image, distinct matrices are allowed for each color channel.. Here we describe a method, called DCTune, for design of color quantization matrices that is based on a model of the visibility of quantization artifacts. The model describes artifact visibility as a function of DCT frequency, color channel, and display resolution and brightness. The model also describes summation of artifacts over space and frequency, and masking of artifacts by the image itself. The DCTune matrices are different from the de facto JPEG matrices, and appear to provide superior visual quality at equal bit-rates.

In the present work we analyze the performance of orthogonal and Biorthogonal wavelet filters for image compression on variety of test images. The test images are of different size and resolution. The compression performance is measured, objectively peak signal to noise ratio and subjectively visual quality of image and it is found that Biorthogonal wavelets outperform the orthogonal ones in both the criteria.

Future high resolution instruments planned by CNES to succeed SPOT5 will lead to higher bit rates because of the increase in both resolution and number of bits per pixel, not compensated by the reduced swath. Data compression is then needed, with compression ratio goals higher than the 2.81 SPOT5 value obtained with a JPEG like algorithm. Compression ratio should rise typically to 4-6 values, with artifacts remaining unnoticeable : SPOT5 algorithm performances have clearly to be outdone. On another hand, in the framework of optimized and low cost instruments, noise level will increase. Furthermore, the Modulation Transfer Function (MTF) and the sampling grid will be fitted together, to -at least roughly-satisfy Shannon requirements. As with the Supermode sampling scheme of the SPOT5 Panchromatic band, the images will have to be restored (deconvolution and denoising) and that renders the compression impact assessment much more complex. This paper is a synthesis of numerous studies evaluating several data compression algorithms, some of them supposing that the adaptation between sampling grid and MTF is obtained by the quincunx Supermode scheme. The following points are analyzed : compression decorrelator (DCT, LOT, wavelet, lifting), comparison with JPEG2000 for images acquired on a square grid, compression fitting to the quincunx sampling and on board restoration (before compression) versus on ground restoration. For each of them, we describe the proposed solutions, underlining the associated complexity and comparing them from a quantitative and qualitative point of view, giving the results of experts analyses.

Images form integral part of interactive services broadcasted in digital television. Natural images compression is always trade-off between visual quality and file size. Annoying artifacts arise in highly compressed images. The scope of this article is to investigate the possibilities of image compression enhancement for interactive services. The aim is to decrease size of the image file in such way that the following post-processing compensates the drop of the visual quality. A low-complex post-processing technique is evaluated. Proposed solution is suitable for decreasing downloading time of the images in digital video broadcasting services.

At the present time, block-transform coding is probably the most popular approach for image compression. For this approach, the compressed images are decoded using only the transmitted transform data. We formulate image decoding as an image recovery problem. According to this approach, the decoded image is reconstructed using not only the transmitted data but, in addition, the prior knowledge that images before compression do not display between-block discontinuities. A spatially adaptive image recovery algorithm is proposed based on the theory of projections onto convex sets. Apart from the data constraint set, this algorithm uses another new constraint set that enforces between-block smoothness. The novelty of this set is that it captures both the local statistical properties of the image and the human perceptual characteristics. A simplified spatially adaptive recovery algorithm is also proposed, and the analysis of its computational complexity is presented. Numerical experiments are shown that demonstrate that the proposed algorithms work better than both the JPEG deblocking recommendation and our previous projection-based image decoding approach

Focal plane processing applications present a growing computing need for portable telecomputing and videoputing systems. This paper demonstrates the integration of digital processing, analog interface circuitry, and thin film OE devices into a compact computing package. The SIMPil architecture provides a programmable, silicon efficient SIMD processor for effective execution of early image processing applications such as edge detection, convolution, and compression. Results from a demonstration SIMPil node are presented including its microarchitecture, and performance on image processing applications.

In this paper, we present and evaluate a hardware solution for user-driven and packet loss tolerant image compression, especially designed to enable low power image compression and communication over wireless camera sensor networks (WCSNs). The proposed System-on-Chip is intended to be designed as a hardware coprocessor embedded in the camera sensor node. The goal is to relieve the node microcontroller of the image compression tasks and to achieve high-speed and low power image processing. The interest of our solution is twofold. First, compression settings can be changed at runtime (upon reception of a request message sent by an end user or according to the internal state of the camera sensor node). Second, the image compression chain includes a (block of) pixel interleaving scheme which significantly improves the robustness against packet loss in image communication. We discuss in depth the internal hardware architecture of the encoder chip which is planned to reach high performance running in FPGAs and in ASIC circuits. Synthesis results and relevant performance comparisons with related works are presented. (M.L. Kaddachi), [email protected] (A. Soudani), [email protected] (V. Lecuire), [email protected] (K. Torki), [email protected] (L. Makkaoui), [email protected] (J.-M. Moureaux). 0920-5489/$see front matter

A Flexible Representation of Quantum Images (FRQI) is proposed to provide a representation for images on quantum computers in the form of a normalized state which captures information about colors and their corresponding positions in the images. A constructive polynomial preparation for the FRQI state from an initial state, an algorithm for quantum image compression (QIC), and processing operations for quantum images are combined to build the whole process for quantum image processing on FRQI. The simulation experiments on FRQI include storing, retrieving of images and a detection of a line in binary images by applying quantum Fourier transform as a processing operation. The compression ratios of QIC between groups of same color positions range from 68.75 to 90.63% on single digit images and 6.67-31.62% on the Lena image. The FRQI provides a foundation not only to express images but also to explore theoretical and practical aspects of image processing on quantum computers.

In today’s world of electronic revolution digital images play an important role in consumer electronics. Digital images are stored in different formats, each format having its own set of characteristics making it suitable for any specific area of application. This paper analyzes different image file formats which gives an idea about different file formats with their advantages, disadvantages and analyzes their specific areas of application. Examples analyzing certain images, their file formats and their visible properties are also included

The widespread availability of photo editing software has made it easy to create visually convincing digital image forgeries. To address this problem, there has been much recent work in the eld of digital image forensics. There has been little work, however, in the eld of anti-forensics, which seeks to develop a set of techniques designed to fool current forensic methodologies. In this work, we present a technique for disguising an image's JPEG compression history. An image's JPEG compression history can be used to provide evidence of image manipulation, supply information about the camera used to generate an image, and identify forged regions within an image. We show how the proper addition of noise to an image's discrete cosine transform coef cients can suf ciently remove quantization artifacts which act as indicators of JPEG compression while introducing an acceptable level of distortion. Simulation results are provided to verify the ef cacy of this antiforensic technique.

Multiresolution analysis and wavelets provide useful and efficient tools for representing functions at multiple levels of detail. Wavelet representations have been used in a broad range of applications, including image compression, physical simulation, and numerical analysis. In this article, we present a new class of wavelets, based on subdivision surfaces, that radically extends the class of representable functions. Whereas previous two-dimensional methods were restricted to functions defined on ‫ޒ‬ 2 , the subdivision wavelets developed here may be applied to functions defined on compact surfaces of arbitrary topological type. We envision many applications of this work, including continuous level-of-detail control for graphics rendering, compression of geometric models, and acceleration of global illumination algorithms. Level-ofdetail control for spherical domains is illustrated using two examples: shape approximation of a polyhedral model, and color approximation of global terrain data.

Compressing an image is significantly different than compressing raw binary data. compressing images is used by this different compression algorithm. Wavelet transforms used in Image compression methods to provide high compression rates while maintaining good image quality. Discrete Wavelet Transform (DWT) is one of the most common methods used in signal and image compression .It is very powerful compared to other transform because its ability to represent any type of signals both in time and frequency domain simultaneously. In this paper, we will moot the use of Wavelet Based Image compression algorithm- Embedded Zerotree Wavelet (EZW). We will obtain a bit stream with increasing accuracy from ezw algorithm because of basing on progressive encoding to compress an image into . All the numerical results were done by using matlab coding and the numerical analysis of this algorithm is carried out by sizing Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR) for standard Lena Image .Experimental results beam that the method is fast, robust and efficient enough to implement it in still and complex images with significant image compression.

A compressão de dados é um dos factores que mais contribuiu para o grande crescimento das tecnologias da informação e da comunicação. Sem compressão, a maioria dos produtos tecnológicos de consumo e entretenimento, que são hoje banais, nunca teria chegado a existir, destacando-se, entre muitos outros, o DVD, as câmaras fotográficas digitais, os leitores de MP3, o YouTube e o streaming de vídeo, as redes sem fios e a televisão digital. De facto, as tecnologias de compressão multimédia permitem representar a informação de uma forma mais eficiente, reduzindo os grandes volumes de espaço de armazenamento que ocupa e, portanto, a largura de banda que consome para se transmitir nas redes e na Internet. Este livro tem como objectivo principal apresentar uma introdução fundamentada, clara, acessível e abrangente aos conceitos e tecnologias relacionadas com métodos, algoritmos e normas internacionais de compressão de informação multimédia. Ao longo do livro são abordados, entre outros, os seguintes temas: Conceitos e normas sobre compressão de informação multimédia, incluindo texto, imagens, áudio e vídeo; Entropia e algoritmos de compressão sem perdas: Shannon-Fano, Huffman, Aritmética e LZW; Psicoacústica, métodos e normas de compressão de áudio, incluindo voz (ADPCM, vocoders) e áudio de alta-fidelidade (transformadas, MP3, AAC, MP4 e Dolby AC-3); Teoria rate-distortion, métodos de compressão de imagem (DPCM, transformadas DCT, waveletet Haar, quantificadores lineares e não-lineares); Normas internacionais JPEG e JPEG2000; Compressão de vídeo, redundância espacial e temporal, quantificação de vídeo para aplicações multimédia e de videoconferência; Normas MPEG.

Image Compression is a wide area. Compression of Images increasing because the raw images need large amounts of disk space seems to be a big disadvantage during transmission & storage. Even though there are so many compression technique already present a better technique which is faster, memory efficient and simple surely suit the requirements of the user. The work is to explain use of different compression techniques more suitable for particular data compression algorithm based on compression ratio, Bit per Pixel, Mean Square Error and Peak Signal to Noise Ratio. Using all four parameters image compression works and gives compressed image as an output.

On-board image compression systems aim to increase the amount of data stored in the on-board mass memory and transmitted to the ground station. This paper presents an overview and analysis of the state-of-the-art in on-board image compression systems. Compression methods and systems implementations are reviewed. Statistical analysis and developing trends are given. A new architecture of an on-board image compression system for future disaster monitoring multi-satellite missions in LEO is described.

It is well known that the images, often used in variety of computer applications, are difficult to store and transmit. One possible solution to overcome this problem is to use a data compression technique where an image is viewed as a matrix and then the operations are performed on the matrix. Image compression is achieved by using Singular Value Decomposition (SVD) technique on the image matrix. The advantage of using the SVD is the property of energy compaction and its ability to adapt to the local statistical variations of an image. Further, the SVD can be performed on any arbitrary, square, reversible and non reversible matrix of m x n size. In this paper, SVD is utilized to compress and reduce the storage space of an image. In addition, the paper investigates the effect of rank in SVD decomposition to measure the quality in terms of MSE and PSNR.

In this paper, we present a novel no-reference (NR) method to assess the quality of JPEG-coded images using a sequential learning algorithm for growing and pruning radial basis function (GAP-RBF) network. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity factors such as edge amplitude, edge length, background activity and background luminance. Image quality estimation involves computation of functional relationship between HVS features and subjective test scores. Here, the functional relationship is approximated using GAP-RBF network. The advantage of using sequential learning algorithm is its capability to learn new samples without affecting the past learning. Further, the sequential learning algorithm requires minimal memory and computational effort. Experimental results prove that the prediction of the trained GAP-RBF network does emulate the mean opinion score (MOS). The subjective test results of the proposed metric are compared with JPEG no-reference image quality index as well as full-reference structural similarity image quality index and it is observed to outperform both.

Image compression is an implementation of the data compression which encodes actual image with some bits. The purpose 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 raise 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 image 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 applied to compress images by using the Huffman encoding techniques.

Quadtrees are a compact hierarchical method of representation of images. In this paper, we explore a number of hierarchical image representations as applied to binary images, of which quadtrees are a single exemplar. We discuss quadtrees, binary trees, and an adaptive hierarchical method. Extending these methods into the third dimension of time results in several other methods. All of these methods are discussed in terms of time complexity, worst case and average compression of random images, and compression results on binary images derived from natural scenes. The results indicate that quadtrees are the most effective for two-dimensional images, but the adaptive algorithms are more effective for dynamic image sequences. Index Terms-Binary images, hierarchical coding, image coding, image compression, image processing, quadtrees.