1 / 16

An improved full-search-equivalent vector quantization method using the law of cosines

This research presents an enhanced vector quantization method that improves the full-search equivalent process by utilizing the law of cosines. The proposed technique offers efficient computation and memory usage, demonstrated through experimental results on image compression. The study outlines vector quantization, full-search equivalent concept, improved search method, and presents conclusions based on analysis. The method enhances the estimation process for identifying closest code vectors, optimizing the codebook search algorithm.

smohr
Download Presentation

An improved full-search-equivalent vector quantization method using the law of cosines

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. An improved full-search-equivalent vector quantization method using the law of cosines Source: IEEE Signal Processing Letters, vol. 11, issue: 2, Feb. 2004, pp. 247-250. Author: Pan, Z.; Kotani, K.; Ohmi, T. Speaker: Chang-Chu Chen Date: 03/24/2005

  2. Outline • Vector Quantization • FS-equivalent • Improved Search Method • Experimental Result • Conclusions

  3. Vector Quantization (VQ) Image compression technique Codebook 0 1 2 (20,45,…,76) 253 254 Original image Index table 255 Vector Quantization Encoder

  4. Vector Quantization (VQ) Image compression technique Codebook 0 1 2 (20,45,…,76) 253 254 Original image Index table 255 Vector Quantization Decoder

  5. 0 1 2 253 254 255 Codebook search • Find closest code vector • Euclidean distance • Full search • PCA (Principal component analysis) Codebook Image vector Index (20,45,…,76) (21,44,…,78) 2

  6. Euclidean Distance • The dimensionality of vector = kAn input vector v = (v1, v2, …, vk)A codeword u = (u1, u2, …, uk) • The Euclidean distance between v and u • Full Search (FS)To find closest uw , where codebook C of size Nc

  7. u u-v θ v x u θ1 θ2 θ v FS-equivalent (2002 Mielikainen) • law of cosines • a fixed vector x sinceso where

  8. FS-equivalent (cont.1) • Estimationthen • Ifthencode vector u cannot be closest code vector

  9. FS-equivalent (cont.2) 1 4 2 3 • Computation analysis • Offline : • Online : 1 2 3 < > : inner product where (just once) Multiplication * 4 , Addition * 3

  10. Improved Search Method • New estimation by let then

  11. Step 1 Step 2 Compute Update index and Search flowchart Compute of all code vector u yes no yes no yes no

  12. Improved Search Method (cont.) • Compute more efficiently with less memory by , u ux, , θ1 , x ux and let x as mth standard basis vector

  13. Experimental Result • Image : 512 x 512, gray level • Block size : 4 x 4 • Codebook size : 1024

  14. Conclusions • Proposed a new estimation with light computation in full search of codebook. • Compute efficiently

  15. Example 1 • Euclidean distance

  16. Example 2 Y u(3,4,5) X Z

More Related