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Accelerated Vector Quantization for Image Coding using Mean Value Predictive Algorithm

This paper presents an advanced vector quantization (VQ) method to enhance image coding efficiency through the implementation of a mean value predictive algorithm and the Partial Distance Search (PDS) technique. By utilizing fast Euclidean computation and preprocessing, the proposed coding scheme significantly reduces searching time during image encoding, achieving over 95% time savings compared to traditional methods. The simulation results demonstrate improved Peak Signal-to-Noise Ratios (PSNR) while maintaining practical encoding processes, making this approach suitable for high-performance image applications.

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Accelerated Vector Quantization for Image Coding using Mean Value Predictive Algorithm

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  1. Fast vector quantization image coding by mean value predictive algorithm Authors: Yung-Gi Wu, Kuo-Lun Fan Source: Journal of Electronic Imaging 13(2), 324–333 (April2004). Speaker: Meng-Jing Tsai Date: 2012.05.08

  2. Outline • Introductions • Vector Quantization (VQ) • Accelerate Coding Techniques • Partial Distance Search (PDS) Algorithm • Fast Euclidean Computation Technique • Proposed Coding Scheme • Preprocessing process • Practical Encoding Process • Simulation Results • Conclusions

  3. Introduction - VQ 0 1 . . . 0 1 . . . 254 255 N-2 N-1 Initial codebook Training set Training Images LBG Algorithm

  4. Introduction - VQ w h Image Index table Image encoding procedure

  5. Introduction - VQ w h Image Index table Image decoding procedure

  6. Introduction – VQ & PDS The traditional VQ compression system must search from the first codeword to the last one. It costs a lot of time. In order to accelerate the computation, the PDS algorithm provides an effective coding technique.

  7. Introduction - PDS 0 1 2 . . . ＝dmin d > dmin d < dmin ＝dmin 253 254 255 Codebook Vector

  8. Introduction • Fast Euclidean Computation Technique • Look-up Table (LUT)

  9. Proposed Coding Scheme • Two parts • Preprocessing process • A codebook that is sorted by the mean value of each codeword within the codebook. • The sorted mean value table of each codeword. • The 2D fast Euclidean table. • Practical encoding process

  10. Proposed Coding Scheme mean = 108.75 mean value table codebook Practical Encoding Process

  11. Practical Encoding Process PSNR = 20.499 dB

  12. Pratical Encoding Process The searching direction.

  13. Simulation Results • First situation • codebook1 & Lenna • Second situation • codebook2 & Jet, Pepper, Lenna, and Scene

  14. Simulation Results PSNR and window size (codebook1 and 1D fast Euclidean technique) PSNR and window size (codebook1 and 2D fast Euclidean technique) First situation

  15. Simulation Results First situation

  16. Simulation Results First situation

  17. Simulation Results The average arithmetic operation for each pixel needed with the codebook1. First situation

  18. Simulation Results Window size and CPU time Window size and PSNR • Second situation • codebook2 and 2D fast Euclidean technique

  19. Simulation Results The average arithmetic operation for each pixel needed with the codebook2. Second situation

  20. Conclusions The PDS algorithm and the (2D) fast Euclidean technique are used to accelerate the calculation. It needs more memory for extra tables. Compared to a full search algorithm, it saves more than 95% searching time.

  21. Thank you for your listening.

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