This filter has been created following the ideas of Zhou Wang.
It has been coded with the great help of Mfa, who worked on the core functions.
For a given reference video and a given compressed video, it is meant to compute a quality metric, based on perceived visual distortion. Unlike the well-known PSNR measure, it's not purely mathematical, and should correlate much better with human vision.
Some examples can be found here.
A higher MSE (and so lower PSNR) should mean that the compressed clip is a worse image but MSE and PSNR are flawed in this respect as numerous tests have shown. However with SSIM, according to tests carried out on the VQEG dataset, a higher Q (SSIM value) has a much better relation to the visual quality of the compressed clip. Despite this, bear in mind the SSIM metric still isn't perfect.
This filter is designed to compute an SSIM value by two methods, the original one, and a "enhanced" one that weight these results by lumimasking. On the todo list is to include the motion weighting.
This filter has five parameters:
SSIM (clip1, clip2, "results.csv", "averageSSIM.txt", lumimask=true)
clip1 and clip2 are the reference clip and the compressed clip.
"results.csv" is the file where obtained SSIM values will be written (this can be easily read in excel or notepad for those unfamiliar with the comma separated variables format)
lumimasking switch between the two methods.
When the video is closed, the filter will write a file named "averageSSIM.txt" that will contain the global SSIM value.
An SSIM value is between 0 and 1, 1 meaning perfect quality.
To analyse locally the results, you could use the csv files, and manipulate data in any excel-clone. Examples:
In the csv file, when lumimasking is activated, both SSIM values and its weigth is written.
Note:
If you use B frames under xvid, trim the first dummy frame of the xvid clip, and the last frame of the original clip.
$Date: 2004/08/17 20:31:19 $