2020春新课件ppt英文课件slides_chapter17.ppt

* * * * * Image quality measures Example 17.2 By Oge Marques Copyright ? 2011 by John Wiley Sons, Inc. All rights reserved. Image quality measures Objective quality measurement Limitations: They usually require an ‘original’ image (whose quality is assumed to be perfect). This image may not always be available and the assumption that it is ‘perfect’ may be too strong in many cases. They do not correlate well with human subjective evaluation of quality (see next slide). By Oge Marques Copyright ? 2011 by John Wiley Sons, Inc. All rights reserved. Image quality measures Example: most subjects would say that the bottom image (in which an important portion of the image got severely blurred) has significantly worse subjective quality than the one on the center (where the entire image was subject to a mild blurring), even though their RMS errors (measured against the reference image on the left) are comparable. By Oge Marques Copyright ? 2011 by John Wiley Sons, Inc. All rights reserved. Hands-on Tutorial 17.1: Image compression (page 440) By Oge Marques Copyright ? 2011 by John Wiley Sons, Inc. All rights reserved. * * * * * * * * * * * * * * * * * * * * * * * * * * * * By Oge Marques Copyright ? 2011 by John Wiley Sons, Inc. All rights reserved. By Oge Marques Copyright ? 2011 by John Wiley Sons, Inc. All rights reserved. Chapter 17 – Image compression and coding Practical Image and Video ProcessingUsing MATLAB? By Oge Marques Copyright ? 2011 by John Wiley Sons, Inc. All rights reserved. What will we learn? What is the meaning of compressing an image? Which types of redundancy can be exploited when compressing images and how is this usually done? What are the main (lossy and lossless) image compression techniques in use today? How can we evaluate the (subjective) quality of an image that has undergone some type of processing (e.g., lossy compression) in an objective way? What are the mo