It will describes about image compresion standards in detail

1. IMAGE COMPRESSION STANDARDS
BY,
M.SUJITHA,
II-M.SC(CS&IT),
Nadar Sarawathi College Of Arts And Science,
Theni.

2. INTRODUCTION
 Image compression is a type of data
compression applied to digital images.
 It is used to reduce their cost
for storage or transmission.
 Algorithms may take advantage of visual perception.
 Types of Compressions are:
 Lossy image Compression
 Lossless image compression

3. IMAGE COMPRESSION STANDARDS
International Standards
 Binary image compression standards
 CCITT Group 3 Standard (G3)
 CCITT Group 4 Standard (G4)
Continuous one image and video compression standards
 JPEG, JPEG 2000
 MPEG-1, MPEG-2, MPEG-4
 ITU-T H.261, H.263

4. THE JPEG STANDARD
 JPEG is an image compression standard.
 It was developed by “Joint Photographic Experts
Group”.
 JPEG is a lossy image compression method.
 It employs transform coding method using the DCT
(Discrete Cosine Transform).
 JPEG was formally accepted as an international standard
in 1992.

5. BLOCK DIAGRAM FOR JPEG ENCODER

6. MAIN STEPS IN JPEG IMAGE
COMPRESSION
Transform RGB to YIQ or YUV and subsample color.
 DCT on image blocks.
 Quantization.
 Zig- zag ordering and run-length encoding.
 Entropy coding.

7. FOUR COMMONLY USED JPEG MODES
 Sequential Mode
 It is the default JPEG mode
 Each gray level image or color image
component is encoded in a single left-to-right,
top-to-bottom scan.
 Progressive Mode
 Hierarchical Mode
 Lossless Mode
 JPEG-LS

8. HIERARCHICAL MODE
 The encoded image at the lowest resolution is a
compressed low-pass filtered image.
 The higher resolutions provide additional details.
 Progressive JPEG, the Hierarchical JPEG images can be
transmitted in multiple passes progressively by
improving the quality.

9. BLOCK DIAGRAM FOR HIERARCHICAL
JPEG

10. USE OF MOTION IN SEGMENTATION
 Stationary camera
 Background modeling
 Human tracking & extraction
 Moving camera
 3D Reconstruction
 Moving target detecting

11. MOTION SEGMENTATION
 Segmenting the images is based on common motion.
 Gestalt insight: grouping forms the basis of human
perception

12. APPLICATIONS OF MOTION SEGMENTATION
 Object detection
 pedestrian detection
 Tracking
 vehicle tracking
 Robotics
 Surveillance Vechicle Tracking
 Image and video compression
 Scene reconstruction
 Video manipulation / editing
 video matting
 video annotation
 motion magnification Video Editing

13. CHALLENGES: SHORT TERM
1. statue
2. wall
4. grass
3. trees
5. biker
6. pedestrian
Computation of motion Number of objects
Initialization of motion parameters Description of complex motions

14. CHALLENGES: LONG TERM
Batch processing vs Incremental processing
Maintain existing groups Adding new groups

15. EXPERIMENTAL RESULTS
Mobile-calendar Free throw
Statue Robots Car-map

16. USE OF MOTION CAPTURE DATA

17. IRIS IMAGE SEGMENTATION

18. IRIS SEGMENTATION AND RECOGNITION

19. IMAGE SEGMENTATION RESULTS

20. Joint feature tracking
 Incorporation of neighboring feature motion
 Improved performance in areas of low-texture or repetitive texture
Detection of articulated motion
 Motion based approach for learning high-level human motion models
 Segment and track human motion in varying pose, scale, and lighting
conditions
 View invariant pose estimation
Iris segmentation
 Graph cuts based dense segmentation using texture and intensity
 Combines appearance and eye geometry
 Handles non-ideal iris image with occlusion, illumination changes, and eye
rotation
CONCLUSION

21. THANKYOU….