Explain about Wavelet Coding
Wavelet Coding:
The wavelet coding is based on the idea that the coefficients of a transform that decorrelates the pixels of an image can be coded more efficiently than the original pixels themselves.
If the transform's basis functions—in this case wavelets—pack most of the important visual information into a small number of coefficients, the remaining coefficients can be quantized coarsely or truncated to zero with little image distortion.
Figure shows a typical wavelet coding system. To encode a 2J X 2J image, an analyzing wavelet, Ψ, and minimum decomposition level, J - P, are selected and used to compute the image's discrete wavelet transform.
If the wavelet has a complimentary scaling function φ, the fast wavelet transform can be used. In either case, the computed transform converts a large portion of the original image to horizontal, vertical, and diagonal decomposition coefficients with zero mean and Laplacian-like distributions.
Figure A wavelet coding system: (a) encoder; (b) decoder.
Since many of the computed coefficients carry little visual information, they can be quantized and coded to minimize inter coefficient and coding redundancy.
Moreover, the quantization can be adapted to exploit any positional correlation across the P decomposition levels. One or more of the lossless coding methods, including run-length, Huffman, arithmetic, and bit-plane coding, can be incorporated into the final symbol coding step.
Decoding is accomplished by inverting the encoding operations—with the exception of quantization, which cannot be reversed exactly.
The principal difference between the wavelet-based system and the transform coding system is the omission of the transform coder's sub image processing stages.
