Who are differences between coding redundancy, interpixel redundancy,psychovisual redundancy,fidelity criteria and Hoffmann coding?

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What types are redundancy in explained by this topic
Coding redundancy:-
        Like histogram processing assuming that the intensity value of an image are random quantity we use optimal informal coding.
       Assume,
              rk= discrete random variable in the interval [0,L-1] to represent the intensity M×N image.
Interpixel redundancy (spatial and temporal redundancy):-
            Consider the computer generated collection of constant intensity line in the corresponding 2D array.
        All 256 intensity are equally probable.The histogram of image is uniform.
          Becouse the intensity of each line selected randomly.Its pixel are independent of one another in the vertical direction.
        Becouse the pixel along each line are identical they are maximally correlated in the horizontal direction.
        A mapping is said to be reversible if the pixels of the original 2-D intensity array can be reconstructed without error from the transformed data set.
       Otherwise mapping is said to be irreversible.
Psychovisual redundancy:-
           The exist becouse humans perception doesn't involve quantitative analysis of every pixel or luminous value in the image.
         It's elimination is real visual information is possible only becouse the information itself is not essential for normal visual processing.
Fidelity criteria:-
        It was noted that the removal of"irrevelant visual" information involve losses of real or quantitative image information.
Two types of criteria:-
१ Objective fidelity criteria
२ Subjective fidelity criteria
Hoffmann coding:-
           Popular technique for removing coding redundancy is due to Hoffmann.
       Smallest possible number of code symbols of per source symbol.
     In terms of Shannon's first theorem the resulting cide is optimal for fixed value of n.
     It is an entropy based algorithms that relies on an analysis of the frequency of symbol in an array.
     It is compressing a roster image,suppose we have a 5×5 roster image with 8bit colour i.e., 256 different colours.
       This coding reduces average number of like of bits/pixel.
Achieve compression in two step:-
१ Source reduction
२ Code assignment
Step for Hoffmann source reduction and code assignment procedure:-
✍️ Find the gray level probabilities from the image histogram.
✍️Arrange probabilities in reverse order highest at top.
✍️Combine the smallest two by addition always keep sum in reverse order.
✍️Repeat step 3 only two probabilities is left.
✍️By working backward along the tree,generate code by alternating assignment 0 and 1.

        
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