CS4551 Spring 2020 HW #3 rev.
CS4551 Multimedia Software Systems Homework 3 (10%) – Block-based Motion Compensation
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• 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 YouarenotallowedtouseanyJavabuilt-inimageclassmethods,library,ortoolstocompletethis
homework.
o Do not create one mega-size main class.
o DonotchangeanygivenmethodsofMImageclassnorcreateanewclassthatduplicatesMImage
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.
Download the test dataset (video frames of 200 PPM images, Walk_001.ppm ~ Walk_200.ppm) from CSNS. Before programming, check the images using Irfanview.
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.
java CS4551_Doe
Display the following main menu to the user and receive the user’s input.
Main Menu———————————– 1. Block-based Motion Compensation
2. Fast Motion Compensation
3. Quit
Please enter the task number [1-3]:
After performing a selected task, go back to display the menu again.
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CS4551 Spring 2020 HW #3 rev.
1. Task 1 – Block-based Motion Compensation (80 pts)
Write a routine(method) that compensates motions between two images (one is the target image and the other one is the reference image). Receive two images as parameters. Make the routine perform the followings:
• Receive an integer 𝑛 for the macro block size from the user. 𝑛 must be 8, 16, or 24.
• Receive an integer 𝑝 for search window from the user. 𝑝 must be 2, 4, 8, 12, or 16.
• Divide the target image into a set of 𝑛 × 𝑛 macro blocks.
• For each target macro block:
o Estimate one motion vector by finding the best matched macro block (a.k.a. predicted block) in the reference image using 𝑝 for the search area size and MSD (Mean Square Difference) for the matching criteria. When you compute MSD, use Gray values:
Gray = round(0.299 * R + 0.587 * G + 0.114 * B)
o After finding the best matched block, compute the motion vector and the error block
▪ Compute the motion vector using
𝑑 = (𝑑𝑥, 𝑑𝑦) = (𝑡𝑥, 𝑡𝑦) − (𝑟𝑥, 𝑟𝑦)
where (𝑡𝑥, 𝑡𝑦) is the upper left corner location of the target block and (𝑟𝑥, 𝑟𝑦) is the upper left corner location of the best matched block location in the reference frame.
▪ Computetheerrorblock(a.k.a.residualblock)consistingofpixeldifferences(absolutevalues) between the target block and the best matched block (a.k.a. predicted block).
𝑒 = |𝑝𝑖𝑥𝑒𝑙_𝑖𝑛_𝑡𝑎𝑔𝑟𝑒_𝑏𝑙𝑜𝑐𝑘 − 𝑐𝑜𝑟𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑖𝑛𝑔_𝑝𝑖𝑥𝑒𝑙_𝑖𝑛_𝑚𝑎𝑡𝑐h𝑒𝑑_𝑏𝑙𝑜𝑐𝑘|
• Outputs
o Display and Save your error (or residual) image. Your error image is a composition of all error blocks. Scale error values to range [0, 255]. In order to do so, compute 𝑀𝐼𝑁𝑒𝑟𝑟𝑜𝑟 and 𝑀𝐴𝑋𝑒𝑟𝑟𝑜𝑟 and map values from [𝑀𝐼𝑁𝑒𝑟𝑟𝑜𝑟 , 𝑀𝐴𝑋𝑒𝑟𝑟𝑜𝑟 ] to [0, 255] using
𝑆𝑐𝑎𝑙𝑒𝑑𝐸𝑟𝑟𝑜𝑟 = 𝑒 − 𝑀𝐼𝑁𝑒𝑟𝑟𝑜𝑟 × 255 𝑀𝐴𝑋𝑒𝑟𝑟𝑜𝑟 − 𝑀𝐼𝑁𝑒𝑟𝑟𝑜𝑟
o Savetheestimatedmotionvectorstothe“mv.txt”.Itshouldhavemotionvectorsofalltargetblocks. Refer to the, refer sample mv.txt file.
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CS4551
Spring 2020 HW #3 rev.
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Extra credit (20 pts) Implement matching in the half-pixel accuracy:
o Use the best matched block at full-pel position as the starting point, shown as the red dot in the
figure below.
o Checktheneighboringcandidateblocksinhalf-pelpositions,whichareshownasthebluediamonds.
o SelecttheblockwiththeleastMSDamongthe8half-pelblocksandonebestfull-pelblockasthe final matched block in the half-pel block-based motion compensation.
2. Task 2 – Fast Motion Compensation (20 pts)
Implement logarithmic search for matching. Provide an additional menu option for this. Display and save motion vectors and a residual image. Also, display the number of matches performed given 𝑝.
Sample results will be posted on CSNS.
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