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CS4551 Multimedia Software Systems
创建日期：2024-08-09 16:45:14
标签： CS4551
Multimedia Software Systems

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CS4551 Multimedia Software Systems

 Homework2 (15%) – Vector Quantization and DCT Coding

 What to turn in:

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

o Do NOT submit data files.

o You 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:

o You are not allowed to use any Java built-in image class methods, library, or tools to complete this

homework.

o Do not create one mega-size main class.

o Do 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.

o Test your program with all test data.

o If 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.

2

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.

P1 P2

P3 P4

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

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