09-05-2011, 10:36 AM
Presented by:
Ankit Saroch
Rahul Jindal
Kushal
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ABSTRACT
In this project we have implemented the Baseline JPEG standard using MATLAB.
We have done both the encoding and decoding of grayscale images in JPEG.
With this project we have also shown the differences between the compression ratio and time spent in encoding the images with two different approaches viz-a-viz classic DCT and fast DCT. The project also shows the effect of coefficients on the image restored.
The steps in encoding starts with first dividing the original image in 8X8 blocks of sub-images. Then DCT is performed on these sub-images separately. And it is followed by dividing the resulted matrices by a Quantization Matrix. And the last step in algorithm is to make the data one-dimensional which is done by zig-zag coding and compressed by Huffman coding, run level coding, or arithmetic coding.
The decoding process takes the reverse process of encoding. Firstly, the bit-stream received is converted back into two-dimensional matrices and multiplied back by Quantization Matrix. Then, the Inverse DCT is performed and the sub-images are joined together to restore the image.
1. Introduction
Multimedia images have become a vital and ubiquitous component of everyday life.
The amount of information encoded in an image is quite large. Even with the advances in bandwidth and storage capabilities, if images were not compressed many applications would be too costly. The following research project attempts to answer the following questions: What are the basic principles of image compression? How do we measure how efficient a compression algorithm is? When is JPEG the best image compression algorithm? How does JPEG work? What are the alternatives to JPEG? Do they have any advantages or disadvantages? Finally, what is JPEG200?
1.1 What Is an Image?
Basically, an image is a rectangular array of dots, called pixels. The size of the image is the number of pixels (width x height). Every pixel in an image is a certain color. When dealing with a black and white (where each pixel is either totally white, or totally black) image, the choices are limited since only a single bit is needed for each pixel. This type of image is good for line art, such as a cartoon in a newspaper. Another type of colorless image is a grayscale image. Grayscale images, often wrongly called “black and white” as well, use 8 bits per pixel, which is enough to represent every shade of gray that a human eye can distinguish. When dealing with color images, things get a little trickier. The number of bits per pixel is called the depth of the image (or bitplane). A bitplane of n bits can have 2n colors. The human eye can distinguish about 224 colors, although some claim that the number of colors the eye can distinguish is much higher. The most common color depths are 8, 16, and 24 (although 2-bit and 4-bit images are quite common, especially on older systems).
There are two basic ways to store color information in an image. The most direct way is to represent each pixel's color by giving an ordered triple of numbers, which is the combination of red, green, and blue that comprise that particular color. This is referred to as an RGB image. The second way to store information about color is to use a table to store the triples, and use a reference into the table for each pixel. This can markedly improve the storage requirements of an image.
1.2 Transparency
Transparency refers to the technique where certain pixels are layered on top of other pixels so that the bottom pixels will show through the top pixels. This is sometime useful in combining two images on top of each other. It is possible to use varying degrees of transparency, where the degree of transparency is known as an alpha value. In the context of the Web, this technique is often used to get an image to blend in well with the browser's background. Adding transparency can be as simple as choosing an unused color in the image to be the “special transparent” color, and wherever that color occurs, the program displaying the image knows to let the background show through.
Transparency Example:
Non-transparent
Transparent
1.3 File Formats
There are a large number of file formats (hundreds) used to represent an image, some more common then others. Among the most popular are:
• GIF (Graphics Interchange Format)
The most common image format on the Web. Stores 1 to 8-bit color or grayscale images.
• TIFF (Tagged Image File Format)
The standard image format found in most paint, imaging, and desktop publishing programs. Supports 1- to 24- bit images and several different compression schemes.
• SGI Image
Silicon Graphics' native image file format. Stores data in 24-bit RGB color.
• Sun Raster
Sun's native image file format; produced by many programs that run on Sun workstations.
• PICT
Macintosh's native image file format; produced by many programs that run on Macs. Stores up to 24-bit color.
• BMP (Microsoft Windows Bitmap)
Main format supported by Microsoft Windows. Stores 1-, 4-, 8-, and 24-bit images.
• XBM (X Bitmap)
A format for monochrome (1-bit) images common in the X Windows system.
• JPEG File Interchange Format
Developed by the Joint Photographic Experts Group, sometimes simply called the JPEG file format. It can store up to 24-bits of color. Some Web browsers can display JPEG images inline (in particular, Netscape can), but this feature is not a part of the HTML standard.
The following features are common to most bitmap files:
• Header: Found at the beginning of the file, and containing information such as the image's size, number of colors, the compression scheme used, etc.
• Color Table: If applicable, this is usually found in the header.
• Pixel Data: The actual data values in the image.
• Footer: Not all formats include a footer, which is used to signal the end of the data.
1.4 Bandwidth and Transmission
In our high stress, high productivity society, efficiency is key. Most people do not have the time or patience to wait for extended periods of time while an image is downloaded or retrieved. In fact, it has been shown that the average person will only wait 20 seconds for an image to appear on a web page. Given the fact that the average Internet user still has a 28k or 56k modem, it is essential to keep image sizes under control. Without some type of compression, most images would be too cumbersome and impractical for use. The following table is used to show the correlation between modem speed and download time. Note that even high speed Internet users require over one second to download the image.
