adaptive edge-based side-match finite-state classified vector quantization with quadtree map

Adaptive Edge-Based Side-Match Finite-State Classified Vector Quantization with Quadtree Map

IEEE transactions on image processing.VOL. 5, NO. 2, FREBRARY 1996

AuthorsRuey-Feng Chang(張瑞峰 ), CS, CCUWei-Ming Chen(陳偉銘 ), CS, CCU

Outline

Introduction of Vector Quantization (VQ) Basic VQ techniques Adaptive edge-based side-match Simulation results Conclusion

Introduction of Vector Quantization (1/2) Efficient scheme for image compression Component

Codebooks Generated by using the iterative clustering algorithm

Encoder Image is first partitioned into non-overlapping rectangular

blocks (vectors) Each vector is quantized (indexed) to the closest codeword in

the codebook Decoder

Select the corresponding codeword in the codebook via indexes

Encoder side

Decoder side

Clustering

algorithmCodebook

Yi, i = 1, …, Nc

Training image set

Target image

Partition image to NxN blocks (vectors)

Find the closet codeword and index for each vector

Re-constructing image

Find the corresponding codeword via indexes

Introduction of Vector Quantization (2/2) What is closest codeword

Small Euclidean distance

How to generate codebooks Cluster algorithm

K-means Linde-Buzo-Gray (LBG) …

Basic VQ techniquesClassified Vector Quantization (CVQ)

Features Multiple codebooks for specified features of

blocks Advantage

Reduce search time Disadvantage

Extra bits needed

Basic VQ techniquesFinite-State Vector Quantization (FSVQ)

Features Similar to CVQ, but the used codebook is decided

by current codebook and current codeword Advantage

Reduce search space Extra bits aren’t needed

Disadvantage Derailment

Basic VQ techniquesSide-Match Vector Quantization (SMVQ) (1/2)

Features A class of FSVQ, but use the side of upper and

left neighboring blocks to generate the state codebook

Advantage Reduce search space Smoother

Disadvantage Derailment

Basic VQ techniquesSide-Match Vector Quantization (SMVQ) (2/2)

n

jmjj uyyhd

1

21 )()(

m

iini lyyvd

1

21 )()(

)()()( yvdyhdysmd

Adaptive edge-based side-match

Edge Detection Sobel Filter

Classification Non Edge Block (SMVQ) Edge Block (CVQ,SMVQ)

Adaptive edge-based side-matchSobel Filter (1/2)

Sobel Filter can increase the high frequency part of image.

Formula

Gradient :

Θ : threshold for checking if the edge occur

|||| yx GGf

||||,0

||||,1

GyGx

GyGxEage

Adaptive edge-based side-matchSobel Filter (2/2)

Sobel operator Gy = (z3 + 2z6 + z9) – (z1 + 2z4 + z7)

Gx = (z7 + 2z8 + z9) – (z1 + 2z2 + z3)

101

202

101

121

000

121

987

654

321

zzz

zzz

zzz

Image region Mask used to compute Gx Mask used to compute Gy

Adaptive edge-based side-matchQuadtree Map

QTC=1-0011-0001-0011

Problem Each vector need one more bit to determine its class

For example, in a 512 X 512 image with 4 X 4 block size, 16386 bits must be transmitted to the decoder

Solution Quadtree map

Adaptive edge-based side-matchEdge Block(CVQ)

Nonedge blocks encode first. Original SMVQ

The edge blocks are classified into 16 subclasses, according to the neighboring blocks which are edge or nonedge.

Adaptive edge-based side-matchEncode

Adaptive edge-based side-matchDecode

Simulation results (1/5)

Contribution Higher quality with the same bit rate Codebooks size are variable

Test arguments 256 gray level image Image size : 512 x 512 Vector size : 4 x 4

dBMSE

PSNR2

10

255log10

Test criterion

m

i

m

jijij xx

mMSE

1 1

22 )ˆ()1(

Simulation results (2/5)

Simulation results (3/5)

Simulation results (4/5)

Simulation results (5/5)

Conclusion

The classified FSVQ combine the advantages of CVQ and SMVQ

The system complexity is higher