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![2/7 : Convert RGB to YCbCr
Simple color space model: [R,G,B] per pixel
JPEG uses [Y, Cb, Cr] Model
Y (Brightness) = 0.299R + 0.587G + 0.114B
Cb (Color blueness) = -0.1687R - 0.3313G + 0.5B + 128
Cr (Color redness) = 0.5R - 0.4187G - 0.0813B + 128](https://image.slidesharecdn.com/imagecompression-131204091956-phpapp01/75/Image-Compression-18-2048.jpg)
![4/7 : Apply DCT [ Discrete Cosine Transformation ]
2D DCT:
1D DCT:](https://image.slidesharecdn.com/imagecompression-131204091956-phpapp01/75/Image-Compression-21-2048.jpg)
![4/7 : Apply DCT [ Discrete Cosine Transformation ]
Shift operations
From [0, 255]
To [-128, 127]
DCT
Result](https://image.slidesharecdn.com/imagecompression-131204091956-phpapp01/75/Image-Compression-22-2048.jpg)
![5/7 : Quantization [ Quality Factor ]
Quality of the reconstructed image and the achieved
compression can be controlled by a user by selecting a
quality factor [ Q_JPEG ] :
Q_JPEG ranges between 1 to 100
When Q_JPEG is used, the entries in tables in previous slide is
scaled by the factor alpha (α), defined as :
Q_JPEG is 100 for best reproduction](https://image.slidesharecdn.com/imagecompression-131204091956-phpapp01/75/Image-Compression-24-2048.jpg)
![7/7 : Huffman encoding
Values
G
0
0
-1, 1
1
-3, -2, 2, 3
2
-7,-6,-5,-4,5,6,7 3
.
4
.
5
.
.
.
.
.
.
.
.
.
.
.
.
.
.
-32767..32767 15
Real saved values
.
0,1
00, 01, 10, 11
000,001,010,011,100,101,110,111
.
.
.
.
.
.
.
.
.
RLC result:
[0, -3] [0, 12] [0,
3]......EOB
After group number added:
[0,2,00b] [0,4,1100b]
[0,2,00b]
...... EOB
First Huffman coding (i.e. for
[0,2,00b] ):
[0, 2, 00b] => [100b,
00b]
Input : 512 bits
Output : 113 bits
% Red : 22.07 %](https://image.slidesharecdn.com/imagecompression-131204091956-phpapp01/75/Image-Compression-27-2048.jpg)
Uploaded byParamjeet Singh Jamwal
Image Compression
The document discusses image compression techniques in electrical and electronics engineering, detailing lossy and lossless compression methods, specifically focusing on JPEG compression algorithms. It explains concepts like discrete cosine transform (DCT), quantization, and Huffman encoding, along with the practical applications and advantages of image processing. Additionally, it touches upon the comparison between discrete wavelet transform (DWT) and DCT, and lists various applications of image processing in fields like computer vision and medical imaging.
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Image Compression
- 1. B.Tech (EEE) 2007-2011 Image Compression B.Tech(6th Semester) Electrical & Electronics Engineering College Of Engineering Roorkee
- 2. Contents ... Image Compression LossyCompression Lossless Compression JPEG Compression Algorithm DCT v/s DWT Applications of Image Processing Advantages /Disadvantages of Image Processing
- 3. What is ImageCompression ? Application of data compression on digital images Reduce redundancy of the image data Benefits of Image Compression Store data efficiently Transmit data efficiently
- 4. Lossy Compression Decompressionretrieves data different from the original Used to compress multimedia data Streaming media and internet telephony Methods JPEG TIFF MNG PGF
- 5. Original Image BeforeCompression
- 6. Decompressed Image AfterCompression
- 7. Before Compression 186 KB 57053 26KB 31760 After Compression Image Size Colour Used
- 8. Lossless Compression Exactreconstruction of original data Executable programs and source codes Data loss cant be tolerated Methods JPEG 2000 GIF PNG TIFF
- 9. Original Image BeforeCompression
- 10. Decompressed Image AfterCompression
- 11. Before Compression 186 KB 57053 136KB 57053 After Compression Image Size Colour Used
- 12. What is JPEG? Stands for Joint Photographics Experts Group Lossy compression method Mostly used by digital cameras & web usage Not suited for drawing , textual and iconic graphics
- 13. Basics of JPEGCompression Human vision is insensitive to high spatial frequencies JPEG Takes advantage of this by compressing high frequencies more coarsely and storing image as frequency data Losslessly compressed image, 150KB JPEG compressed, 14KB
- 14. The JPEG CompressionAlgorithm Divide image into 8x8 pixel blocks Apply 2D Fourier Discrete Cosine Transform (FDCT) Transform Apply coarse quantization to high spatial frequency components Compress resulting data losslessly and store 8x8 pixel blocks FDCT Frequency Dependent quantization Quantization Table Zig-zag scan RLE Huffman Encoding output
- 15. The JPEG FileStructure Short name SOI Bytes 0xFFD8 Size none Name Start Of Image SOF0 0xFFC0 variable size Start Of Frame (Baseline DCT) SOF2 0xFFC2 variable size Start Of Frame (Progressive DCT) DHT 0xFFC4 variable size DQT 0xFFDB variable size DRI 0xFFDD 2 bytes SOS 0xFFDA variable size RSTn 0xFFD0 … 0xFFD7 none APPn 0xFFEn variable size Application-specific COM EOI 0xFFFE 0xFFD9 variable size none Comment End Of Image Define Huffman Table(s) Define Quantization Table(s) Define Restart Interval Start Of Scan Restart
- 16. 1/7 : Dividedinto 8x8 blocks
- 17. 1/7 : Dividedinto 8x8 blocks
- 18. 2/7 : ConvertRGB to YCbCr Simple color space model: [R,G,B] per pixel JPEG uses [Y, Cb, Cr] Model Y (Brightness) = 0.299R + 0.587G + 0.114B Cb (Color blueness) = -0.1687R - 0.3313G + 0.5B + 128 Cr (Color redness) = 0.5R - 0.4187G - 0.0813B + 128
- 19. 2/7 : ConvertRGB to YCbCr
- 20. 3/7 : Downsampling( optional ) Y is taken every pixel , and Cb,Cr are taken for a block of 2x2 pixels MCU(minimu m coded unit) : The smallest group of data units that is coded. Data size reduces to a half immediately
- 21. 4/7 : ApplyDCT [ Discrete Cosine Transformation ] 2D DCT: 1D DCT:
- 22. 4/7 : ApplyDCT [ Discrete Cosine Transformation ] Shift operations From [0, 255] To [-128, 127] DCT Result
- 23. 5/7 : Quantization LuminanceQuantization Matrix Chrominance Quantization Matrix Each DCT coefficient F(u, v) is divided by the corresponding quantizer step-size parameter Q(u, v) in the quantization matrix and rounded to the nearest integer as
- 24. 5/7 : Quantization[ Quality Factor ] Quality of the reconstructed image and the achieved compression can be controlled by a user by selecting a quality factor [ Q_JPEG ] : Q_JPEG ranges between 1 to 100 When Q_JPEG is used, the entries in tables in previous slide is scaled by the factor alpha (α), defined as : Q_JPEG is 100 for best reproduction
- 25. 5/7 : Quantization DCTresult Quantization Matrix Quantization result
- 26. 6/7 : Zigzagreordering & RLE Quantization result
- 27. 7/7 : Huffmanencoding Values G 0 0 -1, 1 1 -3, -2, 2, 3 2 -7,-6,-5,-4,5,6,7 3 . 4 . 5 . . . . . . . . . . . . . . -32767..32767 15 Real saved values . 0,1 00, 01, 10, 11 000,001,010,011,100,101,110,111 . . . . . . . . . RLC result: [0, -3] [0, 12] [0, 3]......EOB After group number added: [0,2,00b] [0,4,1100b] [0,2,00b] ...... EOB First Huffman coding (i.e. for [0,2,00b] ): [0, 2, 00b] => [100b, 00b] Input : 512 bits Output : 113 bits % Red : 22.07 %
- 28. JPEG Compression Ratio 500KBimage, minimum compression 40KB image, half compression 11KB image, max compression
- 29. Effects of varyingJPEG Compression Ratio Uncompressed image Half compression, Blurring around sharp edges Max compression, 8-pixel blocks apparent, large distortion in high-frequency areas
- 30. DWT v/s DCT Images containing sharp edges/continuous curves Uses more optimal set of functions to represent sharp edges Wavelets are finite in extent Different families of wavelets
- 31. DWT v/s DCT Waveletcompression file size: 1861 bytes compression ratio - 105.6 JPEG compression file size: 1895 bytes compression ratio - 103.8 Source: http://www.barrt.ru/parshukov/about.htm.
- 32. Applications of ImageProcessing Computer Vision Optical Sorting Face Detection Feature Detection Augmented Reality Remote Sensing Medical Image Processing
- 33. Advantages/Disadvantages of ImageProcessing Post-processing High cost Easy Sharing Easy Retrieval Environment Friendly Multiple Use Disadvantages Advantages Easy Storage Extra Knowledge High Maintenance Standardization Shape/Size of detectors
- 34.
- 35. info4eee An initiative forB.Tech (EEE) Student