Developing and comparing an encoding system using vector quantization &

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME
503
DEVELOPING AND COMPARING AN ENCODING SYSTEM USING
VECTOR QUANTIZATION & EDGE DETECTION
Ms. SONALI MEGHARE & Prof. ROSHANI TALMALE
Department of Computer Science and Engineering Tulsiramji Gaikwad-Patil College of
Engineering, RTMNU, Nagpur
ABSTRACT
Developing and comparing an encoding system using VQ and edge detection gives a
real-time image coding. The edge detection gives the result that the compression is perfect on
the basis of finding the edges of the compressed video. The comparison gives the better result
than the existing Haar transform with respect to time. The compression ratio is get increased
using downsample method as compared to Haar transform. Downsample method also gives
the better edge detection than Haar transform. The encoding process applied is independent
of the vector dimensions and does not perform any arithmetic operations. The decision tree
generated by an offline process. Together with pipeline architecture, high speed encoding is
now realizable in a single Chip. A new systolic architecture to realize the encoder of full-
search vector quantization (VQ) for high-speed applications.
INTRODUCTION
Recently a new interest has been arisen in the field of the very low bit rate video
application. The motivation of this new interest lies in the development of new applications
such as videophones, video conferencing, and many others. The major requirements for these
applications are the low capacity for transmission and storage, in order to use the existing
Public Switched Telephone Networks (PSTN) or mobile channels numerous algorithms have
been explored to implement the high compression system, such as model based and object-
based methods. The advent of multimedia has evidenced a merger of computer technology
and television technology. This merger has resulted in the emergence of several applications
such as teleconferencing, videophone and video-on-demand. These applications would not be
possible without an efficient video compression algorithm. Several international
standardization activities are aiming at developing high performance video compression
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING
& TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 3, May-June (2013), pp. 503-511
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2013): 6.1302 (Calculated by GISI)
www.jifactor.com
IJCET
© I A E M E

504
techniques for different applications, e.g. H.261 for video conferencing, MPEGI for CD-
ROM based applications, MPEG2 for broadcast TV etc. Currently the MPEG standardization
group has started an investigative effort towards developing a standard (currently referred to
as MPEG4) for low bit rate video compression. VQ has been considered as an efficient block-
based lossy compression technique. Edge detection provides information on an object’s edge
transitions instead of a full picture of the object.
RELATED WORK
Work done in the area of edge detection and vector quantization is reviewed and focus
has been made on detecting the edges of the digital images. Edge detection is a problem of
fundamental importance in image analysis. In typical images, edges characterize object
boundaries and are therefore useful for segmentation, registration, and identification of
objects in a scene. Edge detection of an image reduces significantly the amount of data and
filters out information that may be regarded as less relevant, preserving the important
structural properties of an image.
OVERVIEW OF VECTOR QUANTIZATION
Quantization is used to reduced the total number of bits needed for a compressed image.
Scalar Quantization:
Maps one sample of input signal to one quantized output.
Vector Quantization:
Set of input Data to single codeword.
Vector quantization is a classical quantization technique from signal processing which allows
the modeling of probability density functions by the distribution of prototype vectors. It was
originally used for data compression. It works by dividing a large set of points (vectors) into
groups having approximately the same number of points closest to them. Each group is
represented by its centroid point; The density matching property of vector quantization is
powerful, especially for identifying the density of large and high-dimensioned data. Since
data points are represented by the index of their closest centroid, commonly occurring data
have low error, and rare data high error. This is why VQ is suitable for lossy data
compression. It can also be used for lossy data correction and density estimation. Vector
quantization, also called "block quantization" or "pattern matching quantization" is often used
in lossy data compression. It works by encoding values from a multidimensional vector space
into a finite set of values from a discrete subspace of lower dimension. A lower-space vector
requires less storage space, so the data is compressed. Due to the density matching property
of vector quantization, the compressed data have errors that are inversely proportional to their
density
Overview of Edge Detection
Edge detection refers to the process of identifying and locating sharp discontinuities
in an image. The discontinuities are abrupt changes in pixel intensity which characterize
boundaries of objects in a scene.Edge detecting an image significantly reduces the amount of
data and filters out useless information, while preserving the important structural properties
in an image.

505
Canny Edge Detector
Edges characterize boundaries and are therefore a problem of fundamental importance
in image processing. list of criteria to improve current methods of edge detection.
The first and most obvious is low error rate. It is important that edges occurring in images
should not be missed and that there be NO responses to non-edges.
The second criterion is that the edge points be well localized. In other words, the distance
between the edge pixels as found by the detector and the actual edge is to be at a minimum.
A third criterion is to have only one response to a single edge. This was implemented
because the first 2 were not substantial enough to completely eliminate the possibility of
multiple responses to an edge.
Low Bit Rate Picture Coding
Very low bit rate image coding is an important problem regarding applications such
as storage on low memory devices or streaming data on the internet. The state of the art in
image compression is to use 2D wavelets. The advantages of wavelet base multiscale nature
and in their ability to separtly present functions that are Piecewise smooth.Their main
problem on the other hand, is that in 2D wavelets are not able to deal with the natural
geometry of images, i.e they cannot separately represent objects that are smooth away from
regular sub manifolds ability to present functions that are piecewise smooth. Their main
problem on the other hand, is that in 2D wavelets are not able to deal with the natural
geometry of images, i.e they cannot sparsely represent objects that are smooth away from
regular sub manifolds
Object Extraction
Enables structured object based coding at different bit streams , and Vector Quantizer
is to encode blocks by simple numbering. The block-based coding has been conventionally
employed by a number of picture coding algorithms such as MPEG1/2, H.263, etc. However,
usually the edges of an object in a picture spread over numbers of blocks. This is the reason
why the block-based coding causes the so-called block distortion in coded images which
considerably degrades the picture quality. To overcome this drawback of the block-based
coding, the object-based coding is adopted so that at the subsequent stage the objects in a
picture can be classified into a number of groups according to their motions and sizes.
Motion Compensator
A motion compensator is a device that decreases the undesirable effects of the relative
motion between two connected objects. Motion compensators are usually placed between a
floating object and a more stationary object, such as a vessel or a structure fixed to the
seabed. The motion compensator does not prevent the motion, but tries to eliminate the
negative effects of the movement.
Geometric Vector Quantization
Geometric Vector Quantization (GVQ), is one type of product vector quantization
methods, in this method the code vectors are inspired by edge related features of the high-
frequency sub bands. The code vectors for a two-level GVQ that we used are composed of
binary-valued blocks reflecting the basic shapes found in the upper sub bands and two locally
adapted intensity values, which indicate minimum and maximum intensities for each coded
block.

506
System Flow for Image
Basic step for Simulation of a Image
Initially generate Do image file in MATLAB
Run Do file in MATLAB
Enter the name of entity in MATLAB
Select the path for input image in MATLAB in temporary folder
Create new project in modelsim
Run VHDL file
After compiling the VHDL code generate Text file
Run Text file then generate matrix
Put the matrix in MATLAB command window
Run the matrix to get the output result
Result of Vector Quantization using image
Fig 1. Input Image
After giving input image in matlab. Import this do file in modelsim for applying VQ and
generate the text file for this input video.
Fig 2. Text matrix for image
After creating the text file copy the text matrix in command window of matlab and run the
file.
Fig 3. Output Result of compress image

507
Result Of Edge Detection
Fig 4. Original image
Fig 5. Result of edge detection by Canny Operator on Image
System Flow for Video
Initially generate Do Video file in MATLAB
Run Do video file in MATLAB
Enter the name of entity in MATLAB
Enter number of frames to be extracted Select the path for input Video in MATLAB
in temporary folder Create new project in modelsim Run VHDL file
After compiling the VHDL code generate Text file
Run Text file then generate matrix
Put the matrix in MATLAB command window
Run the matrix to get the output result
Result of Vector Quantization and Edge detection using Video
Fig 6. Input video

508
Initially create Do file in Matlab. For creating the do file firstly enter the name of entity
that must be match with the entity name of the modelsim. Eg. the entity name in modelsim
is E_LowBitRateEncoding. This entity name is gives in single quotation. Eg.
‘E_LowBitRateEncoding’.
Fig 7. Input frames in modelsim
After giving the do file in modelsim the input frames is done in binary format.
Fig 8. Matrix for input video
After copying the text file in matlab window then run the video player for view the output
compressed video
Fig 9. Output Compressed video

509
Fig 10. Edge detection of compressed video
Result of compressed video using Haar Transform
Comparisons between Haar and Downsample method with respect to time and
compression ratio
Video Time Compression Ratio
Downsample aamir 2 us 94.67%
Haar aamir 1.58 s 75%
Downsample Flower 6 us 99.67%
Haar Flower 10.75 s 75%
Downsample Car 15 us 94.67%
Haar Car 12.81 s 75%
Downsample Man 12 us 94.67%
Haar Man 12.81 s 75%
Downsample Traffic 9 us 78.67%
Haar Traffic 8.38 s 75%
Haar aamir 1.58 s 75%
From the table and the output of the videos it has been cleared that the compression
quality of the downsample method is much good compared with the Haar transform and the
compression ratio also get increased. The edge detection part is also done very accurately in
the downsample method.

510
CONCLUSION
This paper contain Vector Quantization used for lossy data compression, lossy data
correction, and coding parts. it also measure image compression ratio with specified
extracting number of frame which have been selected by input image with image size by
generating text matrix in VHDL run at MATLAB command window. This paper also
describes an edge detection technique which recognize the edges of an image to show that
compression is perfect.
REFERENCES
[l] “A very low bit rate video coding system using adaptive region classified vector
quantization”, National Taiwan University, by Yee-Wen Chen and Mei-Juan chen.
[2] ”Low bit-rate video streaming for face to face teleconference”, by Zhen Wen and
Tomas Huang.
[3] F. Bartolini, V. Cappellini, A. Mecocci, and R. Vagheggi: “A segmentation-based
motion compensated scheme for low-bit video coding”.
[4] Journal of Information Hiding and Multimedia Signal Processing © 2010 ISSN 2073-
4212, by Zhe-Ming Ln
[5] Chanyul kim and Noel E. O’Connor “Low complexity video compression using
moving edge detection based on DCT coefficients”
[6] International Journal of Recent trends in engineering, vol, 1, No. 2, May 2009 on
“edge detection techniques for image segmentation of soft computing approaches”.
[7] University of Strathclyde, U.K., by the GEC Marconi Hirst Research centre by Stathis
P.Voukelatos and John J. Soraghan on “very low bit rate color video coding using
vector quantization”.
[8] International Journal of Image Processing by Raman maini and Dr.Himanshu
Aggarwal on “Study and coparision of various Image edge detection techniques”
[9] N.S. Jayant, P. Noll, Digital Coding of Waveform,Principle and Application to Speech
and Video ,Prentice-Hall
[10] G. Karlsson and M. Vetterli, “Subband Coding ofVideo Signals for Packet Switched
Networks,”Proceeding of SPIE Conf. on visual communicationand image processing
11, Vol. 845, Cambridge, MA
[11] Z. Li and Z. M. Lu, Fast code vector search scheme for 3D mesh model vector
quantization,IETElectronics Letters, vol. 44, no. 2, pp. 104–105, 2008
[12] Z. M. Lu and Z. Li, Dynamically restricted codebook based vector quantization
scheme for mesh geometry compression” Signal, Proc. of Image and Video
Processing, vol. 2, no. 3 pp. 251–260, 2008.
[13] Z. M. Lu and H. Pei, Hybrid Image Compression Scheme Based on PVQ and
DCTVQ,IEICE Trans.Information and Systems, vol. E88-D, no. 10, pp. 2422–2426,
2005.
[14] Raman Maini and J. S. Sobel, "Performance Evaluation of Prewitt Edge Detector for
Noisy Images", GVIP Journal, Vol. 6, Issue 3, December 2006
[15] Li Dong Zhang; Du Yan Bi; "An improved morphological gradient edge detection
algorithm”, Communications and Information Technology, ISCIT 2005.
IEEEInternational Symposium on Volume 2, (s):1280 – 1283, 12-14 Oct. 2005.

511
[16] B. Yang, Z. M. Lu, D. G. Xu, and S. H. Sun, Neighboring pixels based low complexity
predictive vector quantization algorithms for image coding, ACTA ELECTRONICA
SINICA, vol. 31, no. 5, pp. 707–710, 2003
[17] Rital, S.; Bretto, A.; Cherifi, H.; Aboutajdine, D.; combinatorial edge detection
algorithm on noise images", Video/Image Processing and Multimedia Communications
4thEURASIPIEEE Region 8 International Symposium on VIPromCom, (s):351 – 355,
16-19 June 2005.
[18] Gonzalez, R and Woods, R., "Digital Image Processing" 2/E, Prentice Hall Publisher,
2002
[19] Zhao Yu-qian; Gui Wei-hua; ChenZhen-cheng; Tang Jing-tian; Li Ling-yun; "Medical
Images Edge Detection Based on Mathematical Morphology" Engineering in Medicine
and Biology Society, IEEE-EMBS. 27th Annual International Conference, (s):6492 –
6495, 01- 04 Sept. 2004.
[20] S. A. Rizvi and N. M. Nasrabadi, Predictive residual vector quantization, IEEE Trans.
Image Processing, vol. 4, no. 11, pp. 1482–1495, 2003.
[21] P. Gerken,” Object-Based Analysis-Synthesis Coding of Image Sequences at Very
Low Bit Rates,” IEEE Trans. on Circuits and Systems for Video Tech.pp. 228-235
,June 2001.
[22] C. S. Choi and K.Aizawa,” Analysis and Synthesis of Facial Image Sequences in
Model-Based Image Coding,” IEEE Trans. on Circuits and Systems for Video Tech.
pp. 257-275 ,June 2001.
[23] Y. Linde, A. Buzo and R.M. Gray, ” An algorithm for vector quantizer design,”IEEE
Trans.Commun,Vol. COM-28, pp. 84-95, January 2001
[24] J. Canny, A computational approach to edge detection. IEEE Transactions on Pattern
Analysis and Machine Intelligence PAMI-8 6 2000,
[25] S.Anandanarayanan and Dr.S.K.Srivatsa, “A High Performance Novel Image
Compression Technique using Huffman Coding with Edge Dection”, International
Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013,
pp. 17 - 22, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
[26] R. Pushpavalli and G.Sivaradje, “Nonlinear Filtering Technique for Preserving Edges
and Fine Details of Digital Images”, International Journal of Electronics and
Communication Engineering & Technology (IJECET), Volume 3, Issue 1, 2012,
pp. 29 - 40, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472.

Developing and comparing an encoding system using vector quantization &

More Related Content

What's hot (15)

Viewers also liked (20)

Similar to Developing and comparing an encoding system using vector quantization & (20)

More from IAEME Publication (20)

Recently uploaded (20)

Developing and comparing an encoding system using vector quantization &