Hi,
I understand you have applied 2-D DCT on an image in Simulink and performed quantization on the samples. Now, you are looking to perform inverse quantization to retrieve the quantized samples.
Exact original data recovery post-quantization is not possible due to its lossy nature, inverse quantization aims to approximate the original pre-quantization values as closely as possible for further processing, like IDCT (Inverse Discrete Cosine Transform).
Performing Inverse Quantization:
- Implementiation: Typically, inverse quantization involves multiplying the quantized DCT coefficients by the same quantization matrix used during the forward quantization process. This step is crucial for the inverse DCT process to produce a reasonable approximation of the original image.
- Using a Quantization Matrix in Simulink: If you have the quantization matrix or can define it based on the quantization step used, you can implement a custom block (e.g., using a MATLAB Function block) that multiplies the quantized coefficients by this matrix.
- Simulink Block for Inverse DCT: After inverse quantization, use the 2-D Inverse DCT block available in Simulink to transform the coefficients back to the spatial domain. This block approximates the inverse of the DCT transformation applied before quantization.
- Considerations for Codebook or Matrix: While supplying a codebook or using the transpose/inverse of the DCT matrix isn't directly applicable for inverse quantization, understanding the quantization process and the quantization matrix used can guide you in correctly scaling the quantized coefficients during inverse quantization.
References:
- For details on implementing custom functionality with MATLAB code in Simulink, refer to the documentation of MATLAB Function block: https://www.mathworks.com/help/simulink/slref/matlabfunction.html.
- To learn more about the 2-D Inverse DCT block and its usage in Simulink, visit: https://www.mathworks.com/help/images/ref/idct2.html
Hope this helps!
