Online Video Characteristics and Transcoding Time Dataset
Donated on 5/18/2015
The dataset contains a million randomly sampled video instances listing 10 fundamental video characteristics along with the YouTube video ID.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Regression
Feature Type
Integer, Real
# Instances
168286
# Features
-
Dataset Information
Additional Information
The presented dataset is composed of two tsv files named 'youtube_videos.tsv' and 'transcoding_mesurment.tsv'. The first contains 10 columns of fundamental video characteristics for 1.6 million youtube videos; It contains YouTube video id, duration, bitrate(total in Kbits), bitrate(video bitrate in Kbits), height(in pixle), width(in pixles), framrate, estimated framerate, codec, category, and direct video link. This dataset can be used to gain insight in characteristics of consumer videos found on UGC(Youtube). The second file of our dataset contains 20 columns(see column names for names) which include input and output video characteristics along with their transcoding time and memory resource requirements while transcoding videos to diffrent but valid formats. The second dataset was collected based on experiments on an Intel i7-3720QM CPU through randomly picking two rows from the first dataset and using these as input and output parameters of a video transcoding application, ffmpeg 4 . In section 6 we will use the second dataset to build a transcoding time prediction model and show the significance of our datasets.
Has Missing Values?
No
Variables Table
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Additional Variable Information
id = Youtube videp id duration = duration of video bitrate bitrate(video) = video bitrate height = height of video in pixles width = width of video in pixles frame rate = actual video frame rate frame rate(est.) = estimated video frame rate codec = coding standard used for the video category = YouTube video category url = direct link to video (has expiration date) i = number of i frames in the video p = number of p frames in the video b = number of b frames in the video frames = number of frames in video i_size = total size in byte of i videos p_size = total size in byte of p videos b_size = total size in byte of b videos size = total size of video o_codec = output codec used for transcoding o_bitrate = output bitrate used for transcoding o_framerate = output framerate used for transcoding o_width = output width in pixel used for transcoding o_height = output height used in pixel for transcoding umem = total codec allocated memory for transcoding utime = total transcoding time for transcoding
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset online_video_characteristics_and_transcoding_time_dataset = fetch_ucirepo(id=335) # data (as pandas dataframes) X = online_video_characteristics_and_transcoding_time_dataset.data.features y = online_video_characteristics_and_transcoding_time_dataset.data.targets # metadata print(online_video_characteristics_and_transcoding_time_dataset.metadata) # variable information print(online_video_characteristics_and_transcoding_time_dataset.variables)
Deneke,Tewodros. (2015). Online Video Characteristics and Transcoding Time Dataset. UCI Machine Learning Repository. https://doi.org/10.24432/C58C9K.
@misc{misc_online_video_characteristics_and_transcoding_time_dataset_335,
author = {Deneke,Tewodros},
title = {{Online Video Characteristics and Transcoding Time Dataset}},
year = {2015},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C58C9K}
}Creators
Tewodros Deneke
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.