- Netflix client for ubuntu install#
- Netflix client for ubuntu full#
- Netflix client for ubuntu code#
- Netflix client for ubuntu download#
not every subject views every distorted video).
Netflix client for ubuntu full#
The second way is more general, and can be used when the test is full sampling or partial sampling Ref_score is the score assigned to a reference video, and is required when differential score is calculated, The value of os is a list of scores, reach voted by a subject, and must have the same length for all distorted videos In this example, ref_videos is a list of reference videos.Įach entry is a dictionary, and must have keys content_id, content_name and path (the path to the reference video file).ĭis_videos is a list of distorted videos.Įach entry is a dictionary, and must have keys content_id (the same content ID as the distorted video's corresponding reference video),Īsset_id, os (stands for "opinion score"), and path (the path to the distorted video file). 'content_id': 0, 'content_name': 'checkerboard', every subject views every distorted video. The first way is only useful when the subjective test is full sampling, There are two ways to construct a dataset file. py files, but JSON-formatted files can be constructed in a similar fashion. BR_SR_MOS - Apply subject bias removal, followed by SR, before calculating MOS, as defined in ( )ĭataset_filepath is the path to a dataset file.ZS_SR_DMOS - Apply z-score transformation, followed by SR, before calculating DMOS.SR_DMOS - Apply SR, before calculating DMOS.ZS_SR_MOS - Apply z-score transformation, followed by SR, before calculating MOS.SR_MOS - Apply subject rejection (SR), as defined in ( ), before calculating MOS.DMOS - Differential MOS, as defined in ( ).MLE_CO_AP2 - Alternative implementation of MLE_CO based on Alternate Projection and per-stimuli confidence interval calculation (AP2).MLE_CO_AP - Alternative implementation of MLE_CO based on Alternate Projection (AP).MLE_CO - MLE model that takes into account only subjects ("Content-Oblivious").MLE - Full maximum likelihood estimation (MLE) model that takes into account both subjects and contents.Here subjective_model are the available subjective models offered in the package, including: output-dir \./output/VQEGHD3_dataset_raw Sureal MLE_CO_AP2 resource/dataset/VQEGHD3_dataset_raw.py -print \ output/NFLX_dataset_public_raw_last4outliers If -print is enabled, output statistics will be printed on the command-line and / or the output directory.īelow are two example usages: sureal MLE_CO_AP2 resource/dataset/NFLX_dataset_public_raw_last4outliers.py -print \ If -output-dir is given, plots will be written to the output directory. This will print usage information: usage: subjective_model dataset_filepath Having -editable allows the changes made in the source to be picked up immediately without re-running pip install.
Netflix client for ubuntu install#
If you want to edit the source, use pip install -editable.
Netflix client for ubuntu code#
The code thus far has been tested on Ubuntu 16.04 LTS and macOS 10.13. To test the source code before installing, run: python -m unittest discover -s test -p '*_test.py' Under Ubuntu, you may also need to install the python-tk (Python 2) or python3-tk (Python 3) packages via apt. Under the root directory, (preferably in a virtualenv), install the requirements: pip install -r requirements.txt
Netflix client for ubuntu download#
To install locally, first, download the source. To install SUREAL via pip, run: pip install sureal SUREAL can be either installed through pip (available via PyPI), or locally. SUREAL is being imported by the VMAF package.Ĭurrently, SUREAL supports Python 3.7. Read this paper and this latest paper for some background. SUREAL is a toolbox developed by Netflix for recovering quality scores from noisy measurements obtained by subjective tests.