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lsegreNTIA
reviewed
Sep 6, 2023
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| This software has not been maintained since 2013 and is provided for archival and research purposes only. | ||
| ITS does not have the funding to perform troubleshooting. | ||
| All souce code was developed for MATLAB® R2013b (8.2). Future versions of MATLAB may cause the code not to run. |
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Suggest: All [souce] source code was developed for MATLAB® R2013b (8.2). The code may not to perform as expected in other versions.
lsegreNTIA
reviewed
Sep 6, 2023
| The VQM software tools provide both standardized and non-standardized methods for measuring the video quality of digital video systems. | ||
| Each VQM tool estimates how people perceive video quality. | ||
| These VQM algorithms compare the processed video (output) with the original video (input). | ||
| These models are suitable when the original video is good quality or better. |
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Suggest: These models are suitable for use when ....
blainNTIA
approved these changes
Sep 6, 2023
| File `psnr.m` calculates peak signal to noise ratio (PSNR) according to ITU-T Rec. J.340. | ||
| It also computes PSNR with variable frame delays removed (PSNR-VFD). These metrics are also available in the BVQM and CVQM software. | ||
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| For each software package, there are three download options: the MATLAB® source, a 32-bit compiled version, and a 64-bit compiled version. In each case, the relevant documentation is provided. | ||
| Each metric is stored in this repository such that for the given version of a given `<name>` from the above table: | ||
| ``` | ||
| + <name> |
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Line 66 has a spelling error of "nmae" in "_pc64_v123"
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| This software has not been maintained since 2013 and is provided for archival and research purposes only. | ||
| ITS does not have the funding to perform troubleshooting. | ||
| All souce code was developed for MATLAB® R2013b (8.2). Future versions of MATLAB may cause the code not to run. |
lsegreNTIA
reviewed
Sep 6, 2023
| - NTIA Video Quality Model for Variable Frame Delay (VQM_VFD), described in [this report](https://its.ntia.gov/publications/details.aspx?pub=2556). Finalized in 2011. | ||
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| The Batch Video Quality Metric (BVQM) Software performs out-of-service, lab bench testing. | ||
| BVQM can processing and analyses of multiple video scenes and multiple video systems at once. |
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BVQM can processing and analyses of multiple > BVQM can process and analyze multiple
lsegreNTIA
reviewed
Sep 6, 2023
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| The Batch Video Quality Metric (BVQM) Software performs out-of-service, lab bench testing. | ||
| BVQM can processing and analyses of multiple video scenes and multiple video systems at once. | ||
| BVQM reads video sequences from files, and reports results to the screen. |
lsegreNTIA
reviewed
Sep 6, 2023
| @@ -32,6 +72,28 @@ Readmes on how to use each metric are available under the name `<name>_pc_readme | |||
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| Note that this distribution does not include the MATLAB Runtime, which must first be installed before running any of the compiled versions of these metrics. The runtime can be installed using `MCRInstaller.exe`, located [here](https://www.mathworks.com/products/compiler/matlab-runtime.html). Be aware that the compiled versions of the metrics were compiled in MATLAB R2013b (8.2). Future versions of MATLAB may cause the code not to run. This issue applies to both the compiled code and the source code. | |||
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Replace "Future versions ,,," with The code may not to perform as expected in other versions.
lsegreNTIA
reviewed
Sep 6, 2023
| INLSA is an algorithm that allows multiple subjective datasets to be fitted to a single subjective scale. | ||
| INLSA computes the fit from a common set of objective metrics. | ||
| Files inlsa.m and pars_inlsa.m implement INLSA. | ||
| File inlsa_demo.m creates made-up data for three (3) experiments and plots that data before and after running INLSA. The user can actually see what INLSA does to the data. Also the user gets a concrete example of how to call INLSA. The user can just replace the made-up data with real data and the use INLSA. |
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File inlsa_demo.m creates sample data for three (3) experiments and plots that data before and after running INLSA. The user can actually see what INLSA does to the data. Also the user gets a concrete example of how to call INLSA. The user can just replace the sample data with real data and then run INLSA.
lsegreNTIA
reviewed
Sep 6, 2023
| ## Calibration Options | ||
| The following calibration options are available: | ||
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| - 'none' No calibration will be performed. Assume that the first frame in original and processed video files align temporally. Run model with default calibration values which assumes that the processed video file is perfectly calibrated. |
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... values, which ...
lsegreNTIA
reviewed
Sep 6, 2023
| The following calibration options are available: | ||
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| - 'none' No calibration will be performed. Assume that the first frame in original and processed video files align temporally. Run model with default calibration values which assumes that the processed video file is perfectly calibrated. | ||
| - 'manual' Read the calibration file created on a previous run. The values on the beginning of each line may be manually modified. |
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...values at the beginning ...
lsegreNTIA
reviewed
Sep 6, 2023
| - 'none' No calibration will be performed. Assume that the first frame in original and processed video files align temporally. Run model with default calibration values which assumes that the processed video file is perfectly calibrated. | ||
| - 'manual' Read the calibration file created on a previous run. The values on the beginning of each line may be manually modified. | ||
| - 'rrcal' Perform reduced reference calibration as given in ntia_tr_06_433a.pdf (except assume no spatial scaling). These algorithms use random processes, which may yield slightly different results from one run to another. It is highly recommended that rrcal results be median filtered across 7 or more different video sequences that have been sent through the same video system (see Calibration Note below). | ||
| - 'rrcalscale' Perform reduced reference calibration as given in ntia_tr_06_433a.pdf, including estimating spatial scaling (e.g., stretch). These algorithms use random processes, which may yield slightly different results from one run to another. It is highly recommended that rrcalscale results be median filtered across 7 or more different video sequences that have been sent through the same video system (see Calibration Note below). |
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can we replace ntia_tr_06_443a.pdf with TR-06-433a as a hotlink? Here and following for ntia_tr_08_433b.pdf
lsegreNTIA
approved these changes
Sep 6, 2023
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I forgot about this pull request. This entire branch became obsolete when we started later changes to this README file. Relevant change requests have been added to the 2024 pull request. |
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All of the code in this repository has been on the ITS website for years, freely available / public domain. Do not review the software.
Only pull request tasks are to review README.MD (content and links), review CVQM.MD (links only), and check that the appropriate MATLAB files have been inserted into the repository.
This pull request will move the remaining video quality software and software descriptions from the ITS website (https://its.ntia.gov/research-topics/video-quality-research/software/) to this archival repository. The newly created repository file CVQM.MD archives web page: https://its.ntia.gov/research-topics/video-quality-research/guides-and-tutorials/cvqm-overview/. The goal is to move all video quality software and, software descriptions that predate 2013 into this repository.
The workflow is (1) Lilli and (Brian or Robert) approve the pull request, (2) Shariq approves the software relocation, and (3) Margaret updates the ITS website.