Image compression will be done in two stages.
First compression is done by mapping spatial image to a domain suitable for the compression where mapping concentrates most of information to first coefficient in transformed domain. For the less information loss second, third .. and so on coefficients are to be taken during the reconstruction of image.
second the image compression is done using the symbol coder to represent the transformed coefficient with according to histogram. Higher the probability of occurrence lower the code length and lower the probability higher the code length.
The transform where the most information concentrates on transformed first coefficient that generates minimum Mean-Square-Error is Karhunen-Loeve Transform. But the problem is image dependent transformation matrix coefficients. In a particular case, KLT is exactly equals to (Discrete Cosine Transform) DCT which is Markovian condition where a pixel is dependent of pixel next to it. Another reason to take the DCT as transformation technique is its reverse periodicity.
Once the transformed coefficients are obtained, symbolic representation for the coefficients is done with Huffman Coding where the code length is probability dependent.
First compression is done by mapping spatial image to a domain suitable for the compression where mapping concentrates most of information to first coefficient in transformed domain. For the less information loss second, third .. and so on coefficients are to be taken during the reconstruction of image.
second the image compression is done using the symbol coder to represent the transformed coefficient with according to histogram. Higher the probability of occurrence lower the code length and lower the probability higher the code length.
The transform where the most information concentrates on transformed first coefficient that generates minimum Mean-Square-Error is Karhunen-Loeve Transform. But the problem is image dependent transformation matrix coefficients. In a particular case, KLT is exactly equals to (Discrete Cosine Transform) DCT which is Markovian condition where a pixel is dependent of pixel next to it. Another reason to take the DCT as transformation technique is its reverse periodicity.
Once the transformed coefficients are obtained, symbolic representation for the coefficients is done with Huffman Coding where the code length is probability dependent.
