jpeg image compression using DCT

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Transcript of jpeg image compression using DCT

Jpeg Image compression and decompression

CHAPTER 1 PREAMBLE1.1 GENERAL INTRODUCTIONIn todays digital world, when we see digital movie, listen digital music, read digital mail, store documents digitally, making conversation digitally, we have to deal with huge amount of digital data. So, data compression plays a very significant role to keep the digital world realistic. If there were no data compression techniques, we would have not been able to listen to songs over the Internet, see digital pictures or movies, or we would have not heard about video conferencing or telemedicine. How data compression made it possible? What are the main advantages of data compression in digital world? There may be many answers but the three obvious reasons are the saving of memory space for storage, channel bandwidth and the processing time for transmission. Every one of us might have experienced that before the advent MP3, hardly 4 or 5 songs of wav file could be accommodated. And it was not possible to send a wav file through mail because of its tremendous file size. Also, it took 5 to 10 minutes or even more to download a song from the Internet. Now, we can easily accommodate 50 to 60 songs of MP3 in a music CD of same capacity. Because, the uncompressed audio files can be compressed 10 to 15 times using MP3 format and we have no problem in sending any of our favorite music to our distant friends in any corner of the world. Also, we can download a song in MP3 in a matter of seconds. This is a simple example of significance of data compression. Similar compression schemes were developed for other digital data like images and videos. Videos are nothings but the animations of frames of images in a proper sequence at a rate of 30 frames per second or higher. A huge amount of memory is required for storing video files. The possibility of storing 4/5 movies in DVD CD now rather than we used 2/3 CDs for a movie file is because compression. We will consider here mainly the image compression techniques.

1.2 JPEG IMAGEDept of IT, Dr AIT Page 1

Jpeg Image compression and decompression

JPEG is the most common image format used by digital cameras and other photographic image capture devices for storing and transmitting photographic images on the World Wide Web. JPEG compression is used in a number of image file formats these format variations are often not distinguished and are simply called JPEG. The term "JPEG" is an acronym for the Joint Photographic Experts Group which created the standard Image data compression is concerned with minimizing the number of bits required to represent an image with no significant loss of information. Image compression algorithms aim to remove redundancy present in the data (correlation of data) in a way which makes image reconstruction possible; this is called information preserving compression Perhaps the simplest and most dramatic form of data compression is the sampling of band limited images, where an infinite number of pixels per unit area are reduced to one sample without any loss of information. Consequently, the number of samples per unit area is infinitely reduced. Transform based methods better preserve subjective image quality, and are less sensitive to statistical image property changes both inside a single images and between images. Prediction methods provide higher compression ratios in a much less expensive way. If compressed images are transmitted an important property is insensitivity to transmission channel noise. Transform based techniques are significantly less sensitivity to channel noise. If transform coefficients are corrupted during transmission, the resulting image is spread homogeneously through the image or image part and is not too disturbing. Applications of data compression are primarily in transmission and storage of information. Image transmission applications are in broadcast television, remote sensing via satellite, military communication via aircraft, radar and sonar, teleconferencing, and computer communications.

CHAPTER 2 LITERATURE SURVEYGregory K. Wallace Dept of IT, Dr AIT Page 2

Jpeg Image compression and decompression

Multimedia Engineering Digital Equipment Corporation Maynard, Massachusetts Submitted in December 1991 for publication in IEEE Transactions on Consumer Electronics The JPEG Still Picture Compression Standard 2.1 ABSTRACT For the past few years, a joint ISO/CCITT committee known as JPEG (Joint Photographic Experts Group) has been working to establish the first international compression standard for continuous-tone still images, both grayscale and color. JPEGs proposed standard aims to be generic support a wide variety of applications for continuous-tone images. To meet the differing needs of many applications, the JPEG standard includes two basic compression methods, each with various modes of operation. A DCT-based method is specified for lossy compression, and a predictive method for lossless compression. JPEG features a simple lossy technique known as the Baseline method, a subset of the other DCT-based modes of operation. The Baseline method has been by far the most widely implemented JPEG method to date, and is sufficient in its own right for a large number of applications. This article provides an overview of the JPEG standard, and focuses in detail on the Baseline method. Advances over the past decade in many aspects of digital technology especially devices for image acquisition, data storage, and bitmapped printing and display - have brought about many applications of digital imaging. However, these applications tend to be specialized due to their relatively high cost. With the possible exception of facsimile, digital images are not commonplace in general-purpose computing systems the way text and geometric graphics are. The majority of modern business and consumer usage of photographs and other types of images take place through more traditional analog means. U. S. Mohammed1 and W. M. Abd-Elhafiez Department of Electrical Engineering, Assiut University, Assiut 71516, Egypt Received Jan 11, 2009; Revised Feb. 15, 2009; Accepted June 25, 2009 New Approaches for DCT-Based Image Compression Using Region of Interest Scheme Dept of IT, Dr AIT Page 3

Jpeg Image compression and decompression

2.2 ABSTRACT In this paper, new techniques for the DCT image coding based in pixels classifications are proposed. Two image coding approaches based on the object extraction are presented to study the effect of the object based image coding on the compression quality. Moreover, modification of the traditional JPEG method based on Region-of-interest coding is achieved. In the beginning, the image is subdivided into a block of pixels with block size of N x N. Firstly; the block must be classified as foreground block or background block based on a pre-processing step. The foreground blocks will be compressed via JPEG technique but with significant quantized coefficients and the DC coefficient only from one block in the background is used to code it. The simulation result shows that the proposed technique provides competitive compression performance relative to the most recent image compression techniques. Image compression maps an original image into a bit stream suitable for storage or transmission over suitable channel in a digital medium, such as multimedia communications, integrated services digital networks (ISDN), storage of medical images, archiving of finger prints and transmission of remote sensing images. The number of bits required to represent the coded image should be smaller than that required for the original image, so that one can use less communication time or storage space. A fundamental goal of data compression is to reduce the volume of data for transmission or storage while maintaining an acceptable fidelity or image quality. Consequently, pixels must not always be reproduced exactly as the originated also, the human visual system (HVS) should not detect the difference between original image and reproduced image.

Dept of IT, Dr AIT

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Jpeg Image compression and decompression

CHAPTER 3 IMAGE CLASSIFICATION AND DIGITIZATIONIn general images can be defined as any two dimensional function f(x, y) where x, y are spatial coordinates, and amplitude of f at any pair of coordinates(x,y) is called intensity or gray level of the image at that point. 3.1 DIGITAL IMAGE When x, y and the amplitude values of f are all finite, discrete quantities, we call the image a digital image.

Fig 3.1 A Digital Image 3.1.1 Pixel: A pixel is a single point in a graphic image. Graphics monitors display pictures by dividing the display screen into thousands (or millions) of pixels, arranged in rows and columns. The pixels are so close together that they appear connected. The number of bits used to represent each pixel determines how many colors or shades of gray can be displayed. For example, in 8-bit color mode, the color monitor uses 8 bits for each pixel, making it possible to display 2 to the 8th power (256) different colors or shades of gray.

Dept of IT, Dr AIT

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Jpeg Image compression and decompression

3.2 IMAGE TYPES The different types of images are1. Binary Images 2. Indexed Images 3. Intensity Images

4. Multi-frame Images5. RGB Images.

3.2.1 Binary image: An image contains only black and white pixels. In MATLAB, a binary image is represented by a uint8 or double logical matrix containing 0's and 1's (which usually represent black and white, respectively). A matrix is logical when its "logical flag" is turned "on." We often use the variable name BW to represent a binary image in memory.imb 2w r bga g 2r y r bin g2 d




Fig 3.2 (a) Binary Image (b) Intensity image (c) RGB image 3.2.2 Indexed image: An image pixel values are direct indices into an RGB color map