Most quantizer based data hiding schemes use uniform quantizer, which is not optimal if the host signal is not uniformly distributed. In this project, we design a quantizer that is not only pdf -matched but also more suited to embedding than the Linde-Buzo-Gray (Lloyd- Max) algorithm for vector (scalar) quantizer design.

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1. Most quantizer based data hiding schemes use uniform quantizer, which is not optimal if the host signal is not uniformly distributed. In this project, we design a quantizer that is not only pdf -matched but also more suited to embedding than the Linde-Buzo-Gray (Lloyd- Max) algorithm for vector (scalar) quantizer design.

2. The purpose of the project is to implement Fibonacci number based uniform quantzer. Note: Fibonacci numbers present a great alternative to binary representation when accuracy is critical, as error correction becomes much easier to implement due to the unique characteristics of this number system.

3. Implementation of Mu law and A law quantization on images

I need help in any of these assignments and that would like a detailed explanation of what exactly happens in the program.

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