UT Arlington EE 5359 - Lecture Notes (27 pages)

Previewing pages 1, 2, 3, 25, 26, 27 of 27 page document View the full content.
View Full Document

Lecture Notes



Previewing pages 1, 2, 3, 25, 26, 27 of actual document.

View the full content.
View Full Document
View Full Document

Lecture Notes

65 views

Exam


Pages:
27
School:
University of Texas at Arlington
Course:
Ee 5359 - Topics in Signal Processing
Topics in Signal Processing Documents

Unformatted text preview:

Babu Hemanth Kumar Aswathappa babuhemanthkumar aswathappa mavs uta edu Guidance Dr K R Rao Introduction In the rate distortion optimization for H 264 I frame encoder the distortion D is measured as the sum of the squared differences between the reconstructed and the original blocks which is MSE Although PSNR and MSE are currently the most widely used objective metrics due to their low complexity and clear physical meaning they were also widely criticized for not correlating well with Human Visual System HVS 2 for a long time The study from previous literature shows that structural similarity metric provides better image assessment than pixel error based metric mean square error and peak signal to noise ratio 2 Mean Squared Error Love It or Leave It So what is the secret of the MSE why is it still so popular What is wrong with the MSE when it does not work well Just how wrong is the MSE in these cases If not the MSE what else can be used 3 What is MSE MSE is a signal fidelity measure The goal of a signal fidelity measure is to compare two signals by providing a quantitative score that describes the degree of similarity fidelity or conversely the level of error distortion between them Suppose that x xi i 1 2 N and y yi i 1 2 N are two finite length discrete signals where N is the number of signal samples and xi and yi are the values of the i th samples in x and y respectively The MSE between the signals is 4 Why do we love MSE The MSE has many attractive features It is simple It is parameter free and inexpensive to compute with a complexity of only one multiply and two additions per sample It is also memoryless the squared error can be evaluated at each sample independent of other samples It has a clear physical meaning it is the natural way to define the energy of the error signal The MSE is an excellent metric in the context of optimization MSE is widely used simply because it is a convention 5 What is wrong with MSE FIG1 Comparison of image fidelity measures for



View Full Document

Access the best Study Guides, Lecture Notes and Practice Exams

Loading Unlocking...
Login

Join to view Lecture Notes and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Lecture Notes and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?