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Blockyness Reduction By Brute Force Matrix Alteration

Blockyness Reduction By Brute Force Matrix Alteration. Bryan Berns & Dirk Werschmoeller. JPEG - History & Importance. JPEG group was formed in 1985 JPEG Compression became standard in the mid-90’s Most widely used format for internet web content

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Blockyness Reduction By Brute Force Matrix Alteration

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  1. Blockyness Reduction By Brute Force Matrix Alteration Bryan Berns & Dirk Werschmoeller

  2. JPEG - History & Importance • JPEG group was formed in 1985 • JPEG Compression became standard in the mid-90’s • Most widely used format for internet web content • Based on biological information biasing of human eye

  3. The JPEG Algorithm • Uses idea that only a few main frequencies contributed to an image’s energy • Breaks down an image to 8 x 8 blocks and its frequency components: - Eliminates unimportant values via a quantization matrix - Encodes these values (DCT coefficients) via Huffman run-length coding

  4. Blockyness • These 8 x 8 individually processed sub-images: • adequately sized for both visual ambiguity and compression ratio - lead to a ‘blocky’ effect if certain image frequencies are discarded

  5. A Brute Force Approach • The standard quantization matrix (Qs) generated to work best on average • However Qs is most likely not optimal for a given image • Brute Force Method generates a new QN which is better adapted to a certain image • Expendable computational power makes Brute Force practical • Process is transparent to the decoder if matrix is transferred.

  6. Brute Force Pseudo-code of a Brute Force method: QMatrix = Quality(x) // Predefined Matrix Repeat { Test_Matrix = RandomChange(QMatrix); Error = EvalError(Test_Matrix,Image); Size = EvalSize(Test_Matrix,Image); Blocky = EvalBlocky(Test_Matrix,Image); // condition // If Size and Error and Blocky < Previous_Values QMatrix = Test_Matrix; If <Convergence Test> Break }

  7. Why Three Constraints? • If size is not bounded, our approach has the possibility of preserving all frequencies (Quality = 100) • If error is not limited, our program has the opportunity to discard all frequency components to obtain zero blockyness

  8. Blockyness Estimation • Very sufficient approach to estimate blockyness of an image: • Computationally efficient • The ideal original image would have a blockyness estimation of ‘1’

  9. Size Estimation • Actual JPEG uses run-length Huffman encoding • Only the relative size is important, so Entropy equation is adequate: • Computationally more efficient than using actual encoding for estimation

  10. Error Estimation • Evaluation of relative error regarding to the original one • Computationally more efficient than mean-squared error

  11. Methods To Perform Changes • Randomly generated sub-’matrix is added to the original one • Evaluating the efficiency of brute force by: -Varying Matrix size -Changing Random Number distribution -Application to a set of images -Comparing with predefining JPEG algorithm

  12. Varying Matrix size

  13. Varying Sub Matrix Generator

  14. Reverse Engineering • In average the changes contributing to the decrease of blockyness are marked by the colored dots • Red dots = more important frequencies for image quality

  15. Application To A Set Of Images

  16. QN Applied To ‘Lena’ • To show value of any quantization major, we must approve it with Lena (who wasn’t in the training group) >> imgcomp('test/lena.png', QN) Estimated Size of Q50: 321458.68918628 Estimated Size of QN : 329104.90241524 Estimated Blockiness of Q50: 1.43134678 Estimated Blockiness of QN : 1.40737543 Error of Q50: 783662.34465455 Error of QN : 767224.82010086

  17. Final Comparison • After many iterations, one can see a reduction in blockyness. Compressed with JPEG Standard (Q = 50) Compressed with QN • Image (macroscopically) blends upwards • Using the optimized quantization matrix helps account for this and reduces blockyness

  18. Summary • Process to decrease the blocky condition (and error, in general) in JPEG-processed files is described • Generated QN also generally improves image compressing quality • Reduction of perceptional errors is proportional to number of program iterations

  19. References Websites: JPEG, http://www.jpeg.org Image Compression, http://www.dspexperts.com/dsp/projects/dctcomp JPEG Image Compression http://www.vectorsite.net/ttdcmp2.html JPEG Compression http://netghost.narod.ru/gff/graphics/book/ch09_06.htm An Introduction to JPEG Encoding http://www.online.redwoods.cc.ca.us/instruct/darnold Datacompression http://www.datacompression.info NASA Vision Group http://vision.arc.nasa.gov/ Books: Digital Image Processing, second edition Gonzales, R.C. ; Woods, R.E.

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