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CS4551 Multimedia Software Systems
创建日期：2024-08-09 16:17:23
标签： CS4551
Multimedia Software Systems

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   CS4551 Multimedia Software Systems

Homework2 () – Vector Quantization and DCT Coding

?What to turn in:

oSubmit source code with necessary files for “compile and run”.

oDo NOT submit data files.

oYou MUST provide a readme.txt file containing all information to help with the grading process.

?If your program produces any compile errors, you will receive 0 automatically no matter how close your program is to the solution.

?Programming requirements:

oYou are not allowed to use any Java built-in image class methods, library, or tools to complete this homework.

oDo not create one mega-size main class.

oDo not change any given methods of MImage class nor create a new class that duplicates MImage

class. Treat MImage as a part of imported library.

oTest your program with all test data.

oIf you do not meet any of the requirements above, you will receive a significant reduction.

0.What your program should do

Name your main application CS4551_[YourLastName].java (e.g. CS4551_Doe.java) and expand the given template program to perform the following tasks.

Receive the input file as command line arguments.

<eg> On Command Prompt

java CS4551_Doe Ducky.ppm

Read a 24bit input PPM image and display the following main menu to the user and receive the user’s input.


Main  Menu

1.Vector  Quantization

2.DCT-based  Coding

3.Quit

Please  enter  the  task  number  [1-3]:

After performing a selected task, go back to display the menu again.


1.Task 1 – Vector Quantization (50 pts)

Compress the 24 bits per pixel input image to 2 bits per pixel using Vector Quantization (VQ). Implement VQ encoding/decoding using the requirements below.


Encoding:

?Input vectors are formed by 2×2 blocks of RGB pixels. Each input vector ?? consists of RGB values of FOUR pixels, P1, P2, P3, and P4, and therefore ?? is 12 dimensional.

Diagram of a 2×2 pixel block of the input image

?Codebook and codebook vectors: The 2-bits per pixel quantization is equivalent to using 8 bits per 4 pixels. Therefore, the VQ should the vector space into 256 (=28) cells and the codebooks should have 256 entries that are centroids of the 256 cells. After the vector quantization, each vector ?? belongs to one cell and each cell number is represented by 1 byte. In order words, after the quantization, each 2×2 block (4×3=12 bytes) is encoded by a 1-byte codebook index. So, the compression ratio is 12.

?Codebook generation: Use K-means clustering algorithm to generate codebook vectors (centroids of cells).

K-means Clustering Algorithm

Inputs: K, number of clusters and the data set (input vectors ??) K is 256 in our case.

Assume that ??[??] store the K centroids. ?? = 0, 1, ? , 255. Each ??[??] is a 12-dimensional vector.

1.Assign randomly generated initial values for the ?? centroids.

2.For each ??,

For each ?? = 0 to 255

If ??[??] is  the  closest  cell  (cluster)  to ?? based  on  the  Euclidean  distance  between ?? and ??[??], assign ?? to ??[??] cluster

3.Update the ?? centroids.

4.Iterate 2 & 3 until the algorithm meets a stopping condition (i.e. no data points change clusters, the sum of the distance is minimized, or the maximum number of iterations is reached.)

?Display the codebook. This is equivalent to displaying ??[??],  ?? = 0, 1, ? ,255.

?Extra credit (10 pts) – Save the quantized image (i.e. indices of all 2×2 blocks) into a PPM file. Given

?? × ?? input image, the quantized image resolution is ??/2 × ??/2. The quantized image is a grayscale image because each pixel is an 8-bit index.

Decoding:

?Given the quantized image and the codebook, for each pixel of the quantized image, recover RGB pixel values of 4 pixels.

?Save the decoded image so that you can compare the output with the input.

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