Did you know that the average video file is about 1 GB in size? That may not seem like much at first, but multiply it by a few hundred, and it becomes clear that videos are notorious space eaters on any computer. Often this issue is complicated by the presence of duplicate videos. For large video collections, this can amount to terabytes worth of space!
Other duplicate video finders are limited to finding only identical copies because they operate by reviewing file sizes, checksums, and other simple measures. Our advanced video fingerprinting technology is able detecting all video copies, even if they are saved in completely different formats, qualities, and resolutions. More details are available in our Find and remove similar video files article.
Our product uses advanced video fingerprinting technology, that allows to detect ALL video copies, including those converted to various video formats or having different quality and resolution.
Duplicate Video Search scans your HD for video duplicates based on a robust set of metrics. Converted, cropped, scaled, and low/high quality all factor into whether or not videos will be flagged as similar. The sliding sensitivity scale is a unique feature that lets you determine the degree to which you'd like DVS to analyze your collection.
4DDiG Duplicate File Deleter is the best duplicate video finder on Windows. Windows-based gadgets, including USB drives, external drives, and other devices can also be scanned on this tool. It uses an advanced MD5 Message-Digest Algorithm to find files quickly with the best accuracy. Using this program, you can erase bothersome duplicates, for example, photographs, videos, and more files without stress. This guide will help you find and erase duplicate files using it.
Duplicate Video Finder Free is a requirement that every PC must have. If you have a variety of films put away on your PC, this tool is for you. It will be beneficial for videographers who invest a lot of energy in shooting videos. Because of its fast working, this video duplicate finder can let loose a lot of space on your disk in only a few minutes.
Outright security in use is the thing that makes it the best duplicate video finder. It won't delete any files without the clients' intent. After checking a specific drive, the program will give you a rundown of video duplicates and deal you to delete the extra files. Some other useful features include:
This duplicate video search software will empower you to track down all music and video files. It will effectively play out the search even if the names of the files are not the same. The binary will guarantee you get the ideal outcomes and find all the duplicate files. Besides, you can look for duplicates in various folders in a single go.
Its high-level Smart Folder feature will allow you to recognize where the first files were, so you can monitor the files that you moved to MusicVideoDuplicates. In addition, you can screen the duplicates that were left unattended in the first folder. The program will likewise automatically compare the duplicate files and the files from your original folder. It requires another $1.99 to MOVE the duplicates.
Considered one of the most mind-blowing PC software, CCleaner can assist you to find duplicate video files on your PC. Before utilizing this tool, take a look at every one of the options for getting the exact outcomes while looking for the duplicate files. Determine your search result by referencing the size and name of the file or showing the date when the video was saved. Add a few exceptional cases while searching to speed up the system.
Duplicate Video Remover Free is a practical instrument that will assist you dealing with your video library. It disposes of redundant files that obstruct your PC. Including an easy-to-use interface, this program permits you to determine the videos and catalogs before deleting files.
It searches files dependent on such attributes as the type, size, or name of the files. This powerful video finder will distinguish every one of the duplicates, even duplicate youtube video quickly and without any problem.
Windows don't have a file search utility, so you want third-party duplicate file removers to assist you with finding and deleting duplicate files. The best duplicate file finder for Windows is 4DDiG Duplicate File Deleter which uses an advanced MD5 Message-Digest Algorithm to find duplicate files quickly with 100% accuracy. With this program, you can get rid of duplicate photographs, videos, and more files easily.
You can find and delete duplicate videos from your PC using any of the above-mentioned software. Download the software right away, and scan your computer for all the files that are saved twice or thrice, and so on. The best among these duplicate video finders is the 4DDiG Duplicate File Deleter. It can get scan your system for all duplicate files in no time.
Windows 10 does not have any in-built duplicate videos finder. So if you wish to find the same videos on your PC, you will need video duplicate finding software. You may download any one of the above-mentioned software to get going.
We hope the above guide answers your "what is the best duplicate file finder" and "how to delete duplicate video files" question and that you presently have a decent duplicate finder downloaded on your PC. You can likewise utilize 4DDiG Duplicate File Deleter since it is the best duplicate video finder available.
I have a lot of video files but also a lot of duplicate with different encoding differences (eg 720p and 4K versions). I wasn't able to find an open source solutions. I tried a Windows paid program who did half the job.
It uses opencv for frame comparison, and ffmpeg for frame extraction (ffmpeg is also for video metadata which is very useful when trying to tell which video to keep !). Since it's open source, you can improve it to fit your needs !
Is youtube officially doing something to remove duplicate videos from youtube?Not only videos, there exists duplicate youtube channels also.Sometimes the original video has less number of views than that of pirated version.
I've recently hacked together a small tool for that purpose. It's still work in progress but usually pretty accurate. The idea is to simply compare time between brightness maxima in the center of the video. Therefore it should work with different resolutions, frame rates and rotation of the video. ffmpeg is used for decoding, imageio as bridge to python, numpy/scipy for maxima computation and some k-nearest-neighbor library (annoy, cyflann, hnsw) for comparison.
Unfortunately the better the comparision the more cpu and memory intensive it gets so I plan on implementing several layers of comparision that begin with very graceful but fast comparision (maby video lengh with a tolerance of 10%) and end with the final comparision that decides whether its really a duplicate (that would be a community vote).
I would transcode the video into a vbr videofile with the exact same settings.then I would look at the bitrate at certain points of time (percentage of the video completed or absolute seconds.. then we would only analyze a portion of the video).same thing as with the picture. Iif the bitrate is greater the average its 1 else its 0.we make a binary string and store it in db and calculate the Levenshtein distance later
It is important that the comparisons be performed using the compute and time resources available: I doubt a solution that takes months to run will be very useful in a dynamic video database. And the size of the database likely makes the use of cloud computing resources unfeasible. So we really care about the local cost of each comparison in several different domains: 1) Data storage, 2) compute resources, and 3) time.
One key cost to consider is that of extracting the data needed from each video for whatever comparison metrics are to be used. Once the extracted data is available, then the cost of performing a comparison must be considered. Finally, the comparisons needed to match all videos to each other must be performed.
The cost of the first two steps is O(1) on the number of videos. The cost of the last step must be worse than O(1), potentially much worse. So our primary goal should be minimizing the costs of the last step, even if it means adding many early, simple steps.
The optimal algorithms for this process will greatly depend on the characteristics of the database, the level to which single and multiple matches exist. If 100% of the videos match some other video, then we will want to minimize the cost of a successful match. However, the more likely case is that matches will be rare, so we will want to minimize the cost of an unsuccessful match. That is to say, if there is a quick and dirty way to say "these two videos can't be matches', then we should use it first, before we even start to confirm a match.
To characterize the database, first do some sampling and hand-matching to estimnate the degree of matching within the database. This experiment should show how the redundant videos "clumped": If a given video had a match, how likely was it to have more than a single match? What percentage of all matches were also part of a multiple match? This process will yield a 'model' of the database (a statistical distribution) that will be used to aid algorithm selection and tune the system.
Going forward I will assume matches are relatively rare. After all, if there are lots of matches, the videos will "clump", effectively making the database smaller, and thus making the problem simpler. Let's assume the problem stays as hard as possible.
I'd advocate a multi-level categorization approach, where we'd build a sequence of algorithms that repeatedly perform the binary decision of "these two videos do not match" / "these two videos may possibly match". Only the very last algorithm in the chain needs to output the answer "These two videos match."
Classification/matching algorithms can fail in either or both of two ways: False Positive (non-matching videos are mislabled as matching) and False Negative (matching videos are mislabeled as non-matching). Each of these wrong decisions has a range of probabilities associated with it, and we want to minimize both. 2b1af7f3a8