This is MTech Dissertation PPT on Image processing and compression.

1. Sunday, October 15, 2023 1

2.  Introduction of Digital Image
 Image Compression
 Image File Format
 Application of Image Compression
 Sampling & Quantization
 Literature Survey
 Problem Statement
 Transformation
 Algorithm
 Result Analysis
 Function Parameter
 Conclusion and Future Work
 Publications
 References
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3.  It is electronic representation of image.
 A image contain the number of pixels.
 Each pixel contain the information about the
colour or intensity.
 All pixel are arranged in Grid Format.
 The number of pixels-per-inch (PPI) is a good
indicator of the resolution.
 Greyscale images are normally displayed in
8-bit manner
 In digital imaging 24-bit mode which
represent true colour.
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4.  Reducing the size
 Encoding information more efficiently.
 Easy and fast Transmission.
 Eliminate space character
 Eliminate duplicate character
 Lossless & Lossy Compression.
 Run Length Encoding Lossless compression
 JPEG compression
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5.  It is very easy to implement.
 It provide a quick method of compression
data.
 Example:
Message for the compression:
AAAABBBAABBBBBCCCCCCCCDABCBAAABBBBCCCD
is replaced by
4A3BAA5B8CDABCB3A4B3CD
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6.  JPEG (Joint Photographic Experts Group).
 GIF (Graphics Interchange Format).
 BMP (Bitmap).
 PNG (Portable Network Graphics).
 TIFF (Tagged Image File Format).
 PSD (Adobe Photoshop Document).
 WEBP
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7.  High Definition TV.
 Satellite remote sensing.
 Fax Transmission.
 Health business for storage of medical
images.
 Passport Department.
 Driving License Department.
 Election Commission Department.
 Web Application.
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8.  Digitizing the coordinate values is called
sampling.
 Digitizing the amplitude values is called
quantization.
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9.  Sugreev Kaur et al. [4] presented a high speed and
area efficient DWT processor based design for
Image Compression applications.
 Nikolay Ponomarenko et al. [7] proposed DCT
based image compression using blocks of size
32x32 is considered.
 Zhigang Gao et al. [6] presented a quality
constrained compression algorithm based on DWT
is proposed.
 Maneesha Guptaam et al. [9] develop some simple
functions to compute the DCT and to compress
images.
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10.  Why there are so many algorithms and
methods for storing and transferring images
in a compressed form.
 We have done work into the principles of
images and its related areas including
compression and decompression, image
displaying and some advanced techniques
used to enhance compression ratios.
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11.  The Discrete Cosine Transformation (DCT) is expresses a
finite sequence of data points in terms of a sum of cosine
functions oscillating at different frequencies.
Figure 1 :DCT of an image
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12.  The Discrete Wavelet Transform (DWT), which is based on
sub-band coding, is found to give up a fast computation of
Wavelet Transform.
 DWT is easy to execute and reduces the computation time
and resources mandatory.
Figure 2 : Transformation based compression
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13. Sunday, October 15, 2023 13
Figure 3 : First & Second level wavelets decomposition

14. Compressed Algorithms:
 Step1: Take the input image (e.g 5.bmp).
 Step2: using wavelets transformation
decomposition at second level (db6.db7,db8)
 Step 3: Find out the block 8x8 after step 2and
applied cosine transformation for each block.
 Step4: apply quantization and sampling
process for each block.
 Step5: Save the compressed image into
5_com.NIT
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15.  Decompressed Algorithms:
 Step 1: read the compressed image
(5_com.NIT)
 Step2: Apply de sampling and de
quantization for each block.
 Step 3: apply inverse cosine transformation.
 Step 4: then combined the block and apply
inverse wavelets transformation.
 Step 5: Save the decompressed image into
5_dcom.bmp
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16. Figure 4 : Image Compression tool
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17.  [1] Liu Chien-Chih, Hang Hsueh-Ming, "Acceleration and Implementation of
JPEG 2000 Encoder on TI DSP platform" Image Processing, 2007. ICIP 2007.
IEEE International Conference on, Vo1. 3, pp. III-329-339, 2005
 [2] H. S. Malvar, “Fast progressive image coding withoutwavelets,” in Proc.
2000 Data Compression Conf., Mar. 2000, pp. 243–252 .
 [3] Anna Saro Vijendran, Vidhya.B" A Hybrid Image Compression Technique
Using Wavelet Transformation - MFOCPN and Interpolation"Global Journal of
Computer Science and Technology Volume 11 Issue Version 1.0 March 2011
 [4] Sugreev Kaur and Rajesh Mehra, “HIGH SPEED AND AREA EFFICIENT 2D
DWTPR OCESSOR BASED IMAGE COMPRESSION Signal & Image Processing : An
International Journal(SIPIJ) Vol.1, No.2, December 2010.
 [5] Kamrul Hasan Talukder and Koichi Harada, “Discrete Wavelet Transform
for Image Compression and A Model of Parallel Image Compression Scheme
for Formal Verification”, , Proceedings of the World Congress on Engineering
2007 Vol I WCE 2007, July 2 - 4, 2007, London, U.K.

18.  [6] Zhigang Gao, Yuan F. Zheng, “ Quality Constrained Compression
Using DWT Based Image Quality Metric”, Dept. of Electrical and
Computer Engineering The Ohio State University
 [7] Nikolay Ponomarenko ,Vladimir Lukin, Karen Egiazarian, Jaakko
Astola. “DCT Based High Quality Image Compression”. IEEE Trans.
Image Proc, vol. 11, pp. 1688–1693
 [8]M. Mozammel Hoque Chowdhury and Amina Khatun," Image
Compression Using Discrete Wavelet Transform",IJCSI International
Journal of Computer Science Issues, Vol. 9, Issue 4, No 1, July 2012
 [9]Maneesha Guptaan and Dr.Amit Kumar Garg,"Analysis Of Image
Compression Algorithm Using DCT",International Journal of Engineering
Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 1, Jan-
Feb 2012,pp.515-521.

19.  [10] Yamanaka, O.; Yamaguchi, T.; Maeda, J.; Suzuki, Y.; “Image
compression using wavelet transform and vector quantization with variable
block size”, IEEE Conference on Soft Computing in Industrial
Applications (SMCia), 2008, Page: 359 – 364.
 [11]Maneesha Guptaan and Dr.Amit Kumar Garg,"Analysis Of Image
Compression Algorithm Using DCT",International Journal of Engineering
Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 1, Jan-
Feb 2012,pp.515-521.
 [12] Syed Ali Khayam, "The Discrete Cosine Transform (DCT): Theory
and Application" Department of Electrical & Computer
Engineering,Michigan State University ,March 10th 2003.

20. THANK YOU
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