Image Compression using Discrete Cosine Transform & Discrete Wavelet Transform

Image Compression addresses the problem of reducing the amount of data required to represent the digital image. Compression is achieved by the removal of one or more of three basic data redundancies: (1) Coding redundancy, which is present when less than optimal (i.e. the smallest length) code words are used; (2) Interpixel redundancy, which results from correlations between the pixels of an image & (3) psycho visual redundancy which is due to data that is ignored by the human visual system (i.e. visually nonessential information). Huffman codes contain the smallest possible number of code symbols (e.g., bits) per source symbol (e.g., grey level value) subject to the constraint that the source symbols are coded one at a time. So, Huffman coding when combined with technique of reducing the image redundancies using Discrete Cosine Transform (DCT) helps in compressing the image data to a very good extent. The Discrete Cosine Transform (DCT) is an example of transform coding. The current JPEG standard uses the DCT as its basis. The DC relocates the highest energies to the upper left corner of the image. The lesser energy or information is relocated into other areas. The DCT is fast. It can be quickly calculated and is best for images with smooth edges like photos with human subjects. The DCT coefficients are all real numbers unlike the Fourier Transform. The Inverse Discrete Cosine Transform (IDCT) can be used to retrieve the image from its transform representation. The Discrete wavelet transform (DWT) has gained widespread acceptance in signal processing and image compression. Because of their inherent multi-resolution nature, wavelet-coding schemes are especially suitable for applications where scalability and tolerable degradation are important. Recently the JPEG committee has released its new image coding standard, JPEG-2000, which has been based upon DWT

Image Compression by Wavelet Transform.

Digital images are widely used in computer applications. Uncompressed digital images require considerable storage capacity and transmission bandwidth. Efficient image compression solutions are becoming more critical with the recent growth of data intensive, multimedia-based web applications. This thesis studies image compression with wavelet transforms. As a necessary background, the basic concepts of graphical image storage and currently used compression algorithms are discussed. The mathematical properties of several types of wavelets, including Haar, Daubechies, and biorthogonal spline wavelets are covered and the Enbedded Zerotree Wavelet (EZW) coding algorithm is introduced. The last part of the thesis analyzes the compression results to compare the wavelet types

High-performance compression of visual information - A tutorial review - Part I : Still Pictures

Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video telephony or multimedia applications, some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this first part, still image coding schemes have been classified into predictive, block transform, and multiresolution approaches. Predictive methods are suited to lossless and low-compression applications. Transform-based coding schemes achieve higher compression ratios for lossy compression but suffer from blocking artifacts at high-compression ratios. Multiresolution approaches are suited for lossy as well for lossless compression. At lossy high-compression ratios, the typical artifact visible in the reconstructed images is the ringing effect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second-generation coding techniques segment the image into semantically meaningful parts. Therefore, parts of these methods have been adapted to work for arbitrarily shaped regions. In order to add another functionality, such as progressive transmission of the information, specific quantization algorithms must be defined. A final step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare stateof- the-art techniques for lossy and lossless compression. The different artifacts of each technique are highlighted and discussed. Also, the possibility of progressive transmission is illustrated

Visual Data Compression for Multimedia Applications

The compression of visual information in the framework of multimedia applications is discussed. To this end, major approaches to compress still as well as moving pictures are reviewed. The most important objective in any compression algorithm is that of compression efficiency. High-compression coding of still pictures can be split into three categories: waveform, second-generation, and fractal coding techniques. Each coding approach introduces a different artifact at the target bit rates. The primary objective of most ongoing research in this field is to mask these artifacts as much as possible to the human visual system. Video-compression techniques have to deal with data enriched by one more component, namely, the temporal coordinate. Either compression techniques developed for still images can be generalized for three-dimensional signals (space and time) or a hybrid approach can be defined based on motion compensation. The video compression techniques can then be classified into the following four classes: waveform, object-based, model-based, and fractal coding techniques. This paper provides the reader with a tutorial on major visual data-compression techniques and a list of references for further information as the details of each metho

Improved image decompression for reduced transform coding artifacts

The perceived quality of images reconstructed from low bit rate compression is severely degraded by the appearance of transform coding artifacts. This paper proposes a method for producing higher quality reconstructed images based on a stochastic model for the image data. Quantization (scalar or vector) partitions the transform coefficient space and maps all points in a partition cell to a representative reconstruction point, usually taken as the centroid of the cell. The proposed image estimation technique selects the reconstruction point within the quantization partition cell which results in a reconstructed image which best fits a non-Gaussian Markov random field (MRF) image model. This approach results in a convex constrained optimization problem which can be solved iteratively. At each iteration, the gradient projection method is used to update the estimate based on the image model. In the transform domain, the resulting coefficient reconstruction points are projected to the particular quantization partition cells defined by the compressed image. Experimental results will be shown for images compressed using scalar quantization of block DCT and using vector quantization of subband wavelet transform. The proposed image decompression provides a reconstructed image with reduced visibility of transform coding artifacts and superior perceived quality

Variable Block Size Motion Compensation In The Redundant Wavelet Domain

Video is one of the most powerful forms of multimedia because of the extensive information it delivers. Video sequences are highly correlated both temporally and spatially, a fact which makes the compression of video possible. Modern video systems employ motion estimation and motion compensation (ME/MC) to de-correlate a video sequence temporally. ME/MC forms a prediction of the current frame using the frames which have been already encoded. Consequently, one needs to transmit the corresponding residual image instead of the original frame, as well as a set of motion vectors which describe the scene motion as observed at the encoder. The redundant wavelet transform (RDWT) provides several advantages over the conventional wavelet transform (DWT). The RDWT overcomes the shift invariant problem in DWT. Moreover, RDWT retains all the phase information of wavelet coefficients and provides multiple prediction possibilities for ME/MC in wavelet domain. The general idea of variable size block motion compensation (VSBMC) technique is to partition a frame in such a way that regions with uniform translational motions are divided into larger blocks while those containing complicated motions into smaller blocks, leading to an adaptive distribution of motion vectors (MV) across the frame. The research proposed new adaptive partitioning schemes and decision criteria in RDWT that utilize more effectively the motion content of a frame in terms of various block sizes. The research also proposed a selective subpixel accuracy algorithm for the motion vector using a multiband approach. The selective subpixel accuracy reduces the computations produced by the conventional subpixel algorithm while maintaining the same accuracy. In addition, the method of overlapped block motion compensation (OBMC) is used to reduce blocking artifacts. Finally, the research extends the applications of the proposed VSBMC to the 3D video sequences. The experimental results obtained here have shown that VSBMC in the RDWT domain can be a powerful tool for video compression

Visual perception based bit allocation for low bitrate video coding

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaves 45-47).by Rajesh Suryadevara.M.S

WAVELET BASED DATA HIDING OF DEM IN THE CONTEXT OF REALTIME 3D VISUALIZATION (Visualisation 3D Temps-Réel à Distance de MNT par Insertion de Données Cachées Basée Ondelettes)

The use of aerial photographs, satellite images, scanned maps and digital elevation models necessitates the setting up of strategies for the storage and visualization of these data. In order to obtain a three dimensional visualization it is necessary to drape the images, called textures, onto the terrain geometry, called Digital Elevation Model (DEM). Practically, all these information are stored in three different files: DEM, texture and position/projection of the data in a geo-referential system. In this paper we propose to stock all these information in a single file for the purpose of synchronization. For this we have developed a wavelet-based embedding method for hiding the data in a colored image. The texture images containing hidden DEM data can then be sent from the server to a client in order to effect 3D visualization of terrains. The embedding method is integrable with the JPEG2000 coder to accommodate compression and multi-resolution visualization. Résumé L'utilisation de photographies aériennes, d'images satellites, de cartes scannées et de modèles numériques de terrains amène à mettre en place des stratégies de stockage et de visualisation de ces données. Afin d'obtenir une visualisation en trois dimensions, il est nécessaire de lier ces images appelées textures avec la géométrie du terrain nommée Modèle Numérique de Terrain (MNT). Ces informations sont en pratiques stockées dans trois fichiers différents : MNT, texture, position et projection des données dans un système géo-référencé. Dans cet article, nous proposons de stocker toutes ces informations dans un seul fichier afin de les synchroniser. Nous avons développé pour cela une méthode d'insertion de données cachées basée ondelettes dans une image couleur. Les images de texture contenant les données MNT cachées peuvent ensuite être envoyées du serveur au client afin d'effectuer une visualisation 3D de terrains. Afin de combiner une visualisation en multirésolution et une compression, l'insertion des données cachées est intégrable dans le codeur JPEG 2000