The structural similarity SSIM index is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. Further variants of the model have been developed in the Image and Visual Computing Laboratory at University of Waterloo and have been commercially marketed. SSIM is used for measuring the similarity between two images. The SSIM index is a full reference metric ; in other words, the measurement or prediction of image quality is based on an initial uncompressed or distortion-free image as reference.
SSIM subsequently found strong adoption in the image processing community. The SSIM paper has been cited over 20, times according to Google Scholar making it one of the highest cited papers in the image processing and video engineering fields.My New Course: Computing and Using Video Quality Metrics: A Course for Encoding Professionals
The difference with respect to other techniques mentioned previously such as MSE or PSNR is that these approaches estimate absolute errors ; on the other hand, SSIM is a perception-based model that considers image degradation as perceived change in structural informationwhile also incorporating important perceptual phenomena, including both luminance masking and contrast masking terms. Structural information is the idea that the pixels have strong inter-dependencies especially when they are spatially close.
These dependencies carry important information about the structure of the objects in the visual scene. Luminance masking is a phenomenon whereby image distortions in this context tend to be less visible in bright regions, while contrast masking is a phenomenon whereby distortions become less visible where there is significant activity or "texture" in the image. The SSIM index is calculated on various windows of an image.
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The individual comparison functions are: . SSIM satisfies the non-negativity, identity of indiscernibles, and symmetry properties, but not the triangle inequality, and thus is not a distance metric. In order to evaluate the image quality, this formula is usually applied only on lumaalthough it may also be applied on color e. YCbCr values. The resultant SSIM index is a decimal value between -1 and 1, and value 1 is only reachable in the case of two identical sets of data and therefore indicates perfect structural similarity.
A value of 0 indicates no structural similarity. The window can be displaced pixel-by-pixel on the image to create an SSIM quality map of the image. In the case of video quality assessment,  the authors propose to use only a subgroup of the possible windows to reduce the complexity of the calculation. It has been shown to perform equally well or better than SSIM on different subjective image and video databases. The proposed weighting is 0. This suggests that edge regions play a dominant role in image quality perception.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
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Already on GitHub? Sign in to your account. As you can see the SSIM values differs quite alot compared to other tools I tried some more versions of ssim that I had available and the vmaf version differs compared to them as well. Can you share the test videos you used? I only validated the implementation against the original matlab implementation of SSIM.
Sorry for the delay! The other implementations you described above, I believe, does not have this step. This should account for the numerical differences. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
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Mapping SSIM and VMAF Scores to Subjective Ratings
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Reload to refresh your session. You signed out in another tab or window.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. A: It is associated with the underlying assumption of VMAF on the subject viewing distance and display size.
Fundamentally, any perceptual quality model should take into account the viewing distance and the display size or the ratio between the two. The same distorted video, if viewed closed-up, could contain more visual artifacts hence yield lower perceptual quality. Effectively, what the VMAF model trying to capture is the perceptual quality of a video displayed from 3H away.
In other words, if you calculate VMAF on the resolution video pair, you are going to predict the perceptual quality of viewing distance 6.
This is going to hide a lot of artifacts, hence yielding a very high score.
Calculating VMAF and PSNR with FFmpeg
One implication of the observation above is that, one should NOT compare the absolute VMAF score of a video with the score of a video obtained at its native resolution -- it is an apples-to-oranges comparison. If, say, for a distorted video of resolution, we still want to predict its quality viewing from 3 times the height not 6. If the distorted video comes with a source reference video of resolution, then the right way to do it is to upsample the video toand calculate the VMAF attogether with its source.
A caveat is, since the VMAF model was not trained with upsampled references, the prediction would not be as accurate as 1. There are psycho-visual evidences, however, suggest that human opinions tend to weigh more heavily towards the worst-quality frames. It is an open question what the optimal way to pool the per-frame scores is, as it also depends on many factors, such as the time scale of the pooling seconds vs minutes.
It is still useful for measuring 4K videos, if you are interested in a relative score. However, if you are interested in an absolute score, say if a 4K video is perceptually acceptable, you may not get an accurate answer.
As of VDK v1. Refer to this section for details. Correspondingly, in terms of the types of video artifacts, it only considers compression artifact and scaling artifact read this tech blog post for more details. The perceptual quality of other artifacts for example, artifacts due to packet losses or transmission errors MAY be predicted inaccurately.
A: Yes, you can. A: VMAF does not guarantee that you get a perfect score in this case, but you should get a score close enough. Whenever there is a numerical change to the VMAF result in running the default model, this number is going to be updated. For anything else, we are going to use the VDK version number. The per-clip feature scores are then fit with the subjective scores to obtain the trained model.
The final score for the clip is arithmetic mean of the per-frame scores. As you can see, there is a re-ordering of the 'temporal pooling' and 'prediction' operators. If the features from a clip are constant, the re-ordering will not have an impact. In practice, we find the numeric difference to be small. If you have a distorted video that was scaled down e. This scales the first input video 0:v and forwards it to VMAF libvmaf with the label mainwhere it is compared against the second input video, 1:v.
See the FFmpeg Filtering Guide for more examples of complex filters, and the Scaling Guide for information about scaling and using different scaling algorithms. Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up.FFmpeg is a great tool for video processing, it basically allows us to manipulate videos any way we like.
Depending on the concrete use case, however, it can be challenging to assemble the right command. As an example, this is a mandatory step for per-title encoding, which I explained in one of my previous blog posts.
The most straightforward way to determine the quality of a video is by watching it and assigning a subjective quality score. For instance, we can rate the quality of our encodes on a scale from 1 to 5, with 1 being bad and 5 being very good.
However, it requires us to watch all of our encodes multiple times, which obviously only scales to a certain degree. Even Netflix and YouTube can not afford to put thousands of people in front of a TV and let them rate the quality of their encodes. Hence, we need a more sophisticated approach to determine the quality of a video. Ideally, this process is completely automated and does not require any human interaction.
Based on the source video, and the encoded video a video quality score is derived. Although I will not go into the details of both metrics, I want to highlight some of the key facts and common pitfalls:.
Without any further delay, here comes the FFmpeg command:. Okay, so that is a lot to take. Let us take a closer look at the individual parts. We start off pretty easily by specifying the paths to our encoded and reference videos. Then, we assemble our filter chain. Please note that you can use different upscaling algorithms e. After scaling the encode, we add some filter options to the reference file.
We scale the reference up to p as well. In addition, we set the framerate to 25fps and the pixel format to yuvp. Depending on your needs you can omit the fps- and the pixel format filter, or add it to the filter chain of the encoded video. Just remember to make sure that your encode and your reference file have the same format and number of frames.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. VMAF is a perceptual video quality assessment algorithm developed by Netflix. Read this techblog post for an overview, or this post for the latest updates and tips for best practices. The core feature extraction library is written in C.
The rest scripting code including the classes for machine learning regression, training and testing VMAF models and etc. We also provide two sample datasets including the video files and the properly formatted dataset files in Python. They can be used as sample datasets to train and test custom VMAF models. Refer to the models page for more details. Since VDK v1. Refer to the VMAF confidence interval page for more details.
For more details, see the Matlab Usage page for more details. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Perceptual video quality assessment based on multi-method fusion. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit.
Latest commit 1e Apr 6, VMAF has been increasingly used in a number of open-source multimedia projects. However, it posed a problem to more deeply integrate VMAF into these projects to perform advanced tasks. The new release candidate API is designed for better interoperrability with encoding optimization. We will deprecate the old API on a future date.
Over time, we have received feedbacks on when VMAF's prediction does not reflect the expected perceptual quality of videos, either they are corner cases where VMAF fails to cover, or new application scenarios which VMAF was not initially intended for.
In response to that, we have created the Google form to allow users to upload their video samples and describe the scenarios. The bad cases are valuable for improving future versions of VMAF.
Users can opt in or out for sharing their sample videos publicly. See the FFmpeg documentation for usage. Refer to this document for detailed usages. Datasets We also provide two sample datasets including the video files and the properly formatted dataset files in Python.
References Refer to the references page. You signed in with another tab or window.Before you start posting please read the forum rules. By posting to this forum you agree to abide by the rules.
When testing the source file against self as a controlwhich should be lossless, I was surprised to find that the VMAF score is not Is this normal? Script: Code:. Originally Posted by HolyWu. Update r3.
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Last edited by WorBry; 1st February at Originally Posted by WorBry. Originally Posted by ChaosKing. Last edited by WorBry; 2nd February at Last edited by WorBry; 6th February at Has anyone else noticed that the VMAF scores in some cases tend to be "too perfect" to measure?
Originally Posted by lansing. Actually, looking at the per-frame scores in that same log, it is just the VMAF score for the first frame that skews the aggregate result, and it looks like it's the motion2 metric which measures temporal difference score of 0 that is responsible for that.
All of the remaining frames have a VMAF score of Perhaps there should be an option to exclude the first frame from the aggregate scores? Last edited by WorBry; 7th February at Good comparison to show that x really has no advantage over x on p materials if we're going for transparent encoding. Now we'll just have to wait for people with high end computer to do the 4K comparison. Last edited by WorBry; 11th February at For better more valid? Last edited by WorBry; 13th February at Last edited by WorBry; 12th February at I did record the libvmaf and ffmpeg PSNR scores also, but they are not as interesting.
For my Image Processing class project, I am filtering an image with various filter algorithms bilateral filter, NL-Means etc. Can anybody help me about:. With respect to an ideal result image, the PSNR computes the mean squared reconstruction error after denoising. Higher PSNR means more noise removed. SSIm has been developed to have a quality reconstruction metric that also takes into account the similarity of the edges high frequency content between the denoised image and the ideal one.
To have a good SSIM measure, an algorithm needs to remove the noise while also preserving the edges of the objects. Hence, SSIM looks like a "better quality measure", but it is more complicated to compute and the exact formula involves one number per pixel, while PSNR gives you an average value for the whole image.
There are a lot of others Image quality measures you can use to evaluate the de-noising capability of various filters in your case NL meansbilateral filter etc. PSNR is the evaluation standard of the reconstructed image quality, and is important feature.
By the other hand the other paramethers are almost equal in sensitivity by both: Gaussian Blur and discriminating Quality. Learn more. Ask Question. Asked 7 years, 11 months ago. Active 6 years, 8 months ago. Viewed 15k times. Can anybody help me about: Does a higher PSNR value means higher quality smoothing getting rid of noise?
Should SSIM value be close to 1 in order to have high quality smoothing? Are there any other metrics or methods to measure smoothing quality? I am really confused.
Any help will be highly appreciated. Thank you. The answer to your question is in the paper icpr