1 / 15

Vector Quantization

Vector Quantization. outline. Introduction Two measurement : quality of image and bit rate Advantages of Vector Quantization over Scalar Quantization The Linde-Buzo-Gray Algotithm Cell-split algorithm Standard VQ encoding Summary. Two measurement. Quality of image.

dimaia
Download Presentation

Vector Quantization

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. Vector Quantization

  2. outline • Introduction • Two measurement : quality of image and bit rate • Advantages of Vector Quantization over Scalar Quantization • The Linde-Buzo-Gray Algotithm • Cell-split algorithm • Standard VQ encoding • Summary

  3. Two measurement • Quality of image

  4. Two measurement (conti) • The amount of compression will be described in terms of the rate, which will be measured in bits per sample. Suppose we have a codebook of size k, and the input vector is of dimension L. We need to use bits to specify which of the code-vectors was selected. The rate for an L-dimensional vector quantizer with a codebook of size K is .

  5. VQ Introduction Source output Encoder Decoder Reconstruction Find closest code-vector Table lookup Group into vectors Unblock

  6. Vector Quantization encoding • VQ was first proposed by Gray in 1984. • First, construct codebook which is composed of codevector. • For one vector being encoding, find the nearest vector in codebook. (determined by Euclidean distance) • Replace the vector by the index in codebook. • When decoding, find the vector corresponding by the index in codebook.

  7. Two important issue • Codevectors in codebook need representative. It affect the quality of de-compressed image a lot. So, how to build a good codebook is an important issue. • Euclidean distance is time-consuming. How to fasten to search for the nearest vector is also an important issue.

  8. LBG Algorithm • Proposed by Linde, Buzo, Gray • The basic idea is to divide a group of vector. To find a most representative vector from one group. Then gather the vectors to form a codebook.

  9. 3 compute the distortion 4 If , stop; otherwise, continue. 5 . Compute new reconstruction valuesGo to Step2.

  10. LBG Algorithm • Divide image into blocks. Then we can view one block as k-dimension vector. Ex: block: 4x4 , consider 512x512 image, which can be divided into blocks. Each block can be viewed 16 dimension vector. • Arbitrarily choose initial codebook. • Set these initial codebook as centroids. Other vectors are grouped. Vectors are in the same group when they have the same nearest centroid. • Again, to find new centroids for every group. Get a new codebooks. Repeat 2,3 steps until the centroids of every group converge.

  11. Standard VQ encoding • For one vector to be encoding, compute the Euclidean distance with every codevectors in codebook, and find the codevector with smallest Euclidean distance.To encode Codebooki codewords

  12. Cell split method(細胞分裂法) For Y, after grouping, find new centorid For Z, after grouping, find new centorid

  13. algorithm • Divide image into blocks. Choose a block (k-dimension) X=(x1, x1,…,x1) as initial vector. • Spit X vector into two vector Y=(y1, y1,…,y1) and Z=(z1, z1,…,z1)yi=xi- ,zi=xi+ • Y and Z are centroids. For all blocks, find the nearest centroid. Re-compute the centroid of blocks and get new centroid Y’ and Z’. • Recursively, do Y’ and Z’. Repeat 2 ,3 step. Until find enough number of codevector.

  14. experience

More Related