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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. Outline. Introductions Vector Quantization (VQ)
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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
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
Introduction - VQ 0 1 . . . 0 1 . . . 254 255 N-2 N-1 Initial codebook Training set Training Images LBG Algorithm
Introduction - VQ w h Image Index table Image encoding procedure
Introduction - VQ w h Image Index table Image decoding procedure
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.
Introduction - PDS 0 1 2 . . . =dmin d > dmin d < dmin =dmin 253 254 255 Codebook Vector
Introduction • Fast Euclidean Computation Technique • Look-up Table (LUT)
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
Proposed Coding Scheme mean = 108.75 mean value table codebook Pratical Encoding Process
Practical Encoding Process PSNR = 20.499 dB
Pratical Encoding Process The searching direction.
Simulation Results • First situation • codebook1 & Lenna • Second situation • codebook2 & Jet, Pepper, Lenna, and Scene
Simulation Results PSNR and window size (codebook1 and 1D fast Euclidean technique) PSNR and window size (codebook1 and 2D fast Euclidean technique) First situation
Simulation Results First situation
Simulation Results First situation
Simulation Results The average arithmetic operation for each pixel needed with the codebook1. First situation
Simulation Results Window size and CPU time Window size and PSNR • Second situation • codebook2 and 2D fast Euclidean technique
Simulation Results The average arithmetic operation for each pixel needed with the codebook2. Second situation
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.