Compression is the reduction in size of data in order to save space or transmission time. And its used just about everywhere. All the images you get on the web are compressed, typically in the JPEG (Joint Photographic Experts Group ), Medical Images, Libraries and Video Conferences, ….etc.

Image compression is the application of data compression. The objective is to reduce redundancy of the image data in order to be able to store or transmit data in an efficient form. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages.

♣ The two fundamental principles used in image compression are redundancy and irrelevancy. ☻Redundancy removes redundancy from the image. ☻irrelevancy omits pixel values which are not noticeable by human eye. ♣ The information in an image can be minimized by removing the redundancy present in it.

♣ There are three types of redundancies: (i)spatial redundancy, which is due to the correlation or dependence between neighboring pixel values (ii)spectral redundancy, which is due to the correlation between different color planes or spectral bands. (iii)temporal redundancy, which is present because of correlation between different frames in images. Image compression aims to reduce the number of bits required to represent an image by removing the spatial and spectral redundancies as much as possible. ♣ JPEG and JPEG 2000 are two important techniques used for image compression

1.The need to efficiently store, manipulate, and transmit large masses of information is growing more rapidly than the capacity of systems to handle it. 2.Uncompressed images take too much space, require larger bandwidth for transmission and longer time to transmit. 3.easier exchange of image files between different devices and applications.

Compression system consists of two components which are encoder and decoder.

Pre processing quantizer Symbol encoder Dequantizer Inverse DCT

8x8 block extraction quantizer Symbol encoder DCT Symbol encoder Dequant- izer Inverse DCT 8x8 block mersor

Original Lena image8x8 block divided Fourier transform DCT transform Wavelet transform Or any linear transform used Quantizer Symbol encoder compressed Lena Image With 4 coefficients Decoding processing

For example let the input pixel matrix is The coefficient of DCT can be compute as follow: Original Lena image

Output DCT matrix DC coefficient is at position 0,0 in the upper left corner of the matrix.

Zigzag sequence

Original Lena imageCompressed Lena image with 4 coefficients Compressed image with 16 coefficients Compressed image with 25 coefficients Compressed image with 40 coefficients Compressed image with 50 coefficients

There are two important compression schemes which are lossy and lossless compression

is a type of data compression in which actual information is lost. the goal is to use lossy compression such that there is not much observable loss in the final product, while saving enormously on file size over lossless compression. In a lossy compression scheme, some of the original information is discarded when it is compressed. Therefore, it is impossible to produce an exact replica of the original image when the image is reconstruct.

Orginal image Medium compression 92% less information High compression 98% less information

Lossless compressors produce an exact replica of the original image. Lossless compression is a compression technique that does not lose any data in the compression process. It assumes you want to get everything back that you put in.

Lossloss CompressionLossy Compression The reconstruction image is identical to the origin The reconstruction image is not identical to the origin Compration ratio 2:1 up to 3:1Compration ratio 20:1 up to 50:1 or more It is not used for image files but is usually applied to text files and other files. It is used for Any type of media file, image files e.g GIF and JPEG for, music e.g MP3 real media and for example of videos files