Manish.firstname.lastname@example.orgShrisantgadgebaba college of engg. & tech, bhusawalElectronics & comm2011 batch
Project presentation image compression by manish myst, ssgbcoet
A <br /> presentation<br />IMAGE COMPRESSION<br />Guided by: Prof. N. A. NEMADE<br />by: Manish kumar<br />Abhishekranjan<br />Prashantwagh<br />
Why image compression?<br /><ul><li>The image compression are required for providing </li></ul> the proper image Format for different devices or <br /> application which can be distinguished On the<br /> basis of their platform and rendering type.<br /><ul><li>For Web based applications
Most displays then were indexed rather than truecolor.
Today it’s still good for diagrams, cartoons, and other nonphotographic</li></ul> images.<br /><ul><li>Lossless encoding good for sharp edges (doesn’t blur).</li></li></ul><li>PNG (Portable Network Graphics)<br /><ul><li>Supports truecolor, greyscale, and palette-based (8 bit) colourmaps.
Convert to frequency space using a two-dimensional DCT
Quantize the frequency space, using more bits for the lower</li></ul>frequencies.<br /><ul><li> Encode the quantized values using Run-length encoding and</li></ul> Huffman coding in a zigzag manner.<br />
File Compression<br />2 Types of File Compression: <br />Lossy Compression<br />Lossless Compression<br />The objective is to reduce redundancy of the image data in order to be able to store data in an efficient form. reducing the file size<br />
Lossy Compression<br />A compression scheme in which certain amounts of data are sacrifices to attain smaller file sizes. It is used when the files do not required extra data. <br />User can choose a loss rate or ratio.<br />JPEG uses lossy compression.<br />
Lossless Compression<br />Lossless compression means that even though the file is compressed (enjoys a smaller file size than an uncompressed image), it will not lose any quality.<br />A lossless image will contain identical data regardless of whether it is compressed pr uncompressed<br />Example of lossless compression included LZW (Lempel-Ziv-Welch) and RLE (Run Length Encoding)<br />
Advantages<br />It is fast to compute compared to many other suitable image transformations,<br /> It usually tends to result in low values for the weights of some of its basis images.<br /> The image that results from deleting low weight components is generally pleasing to the eye.<br />
conclusion<br />While the goal was reached in this particular case, this was not the same for all images tested. <br />Using Neural networks it can be used to represent portions of the DWT coefficients and save some space. Neural networks generally work better on smaller datasets, (with two neurons in the input layer). <br />