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Autres articles (32)
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Monitoring de fermes de MediaSPIP (et de SPIP tant qu’à faire)
31 mai 2013, parLorsque l’on gère plusieurs (voir plusieurs dizaines) de MediaSPIP sur la même installation, il peut être très pratique d’obtenir d’un coup d’oeil certaines informations.
Cet article a pour but de documenter les scripts de monitoring Munin développés avec l’aide d’Infini.
Ces scripts sont installés automatiquement par le script d’installation automatique si une installation de munin est détectée.
Description des scripts
Trois scripts Munin ont été développés :
1. mediaspip_medias
Un script de (...) -
Encoding and processing into web-friendly formats
13 avril 2011, parMediaSPIP automatically converts uploaded files to internet-compatible formats.
Video files are encoded in MP4, Ogv and WebM (supported by HTML5) and MP4 (supported by Flash).
Audio files are encoded in MP3 and Ogg (supported by HTML5) and MP3 (supported by Flash).
Where possible, text is analyzed in order to retrieve the data needed for search engine detection, and then exported as a series of image files.
All uploaded files are stored online in their original format, so you can (...) -
Contribute to documentation
13 avril 2011Documentation is vital to the development of improved technical capabilities.
MediaSPIP welcomes documentation by users as well as developers - including : critique of existing features and functions articles contributed by developers, administrators, content producers and editors screenshots to illustrate the above translations of existing documentation into other languages
To contribute, register to the project users’ mailing (...)
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Detect bad frames in OpenCV 2.4.9
14 mai 2014, par user3630380I know the title is a bit vague but I’m not sure how else to describe it.
CentOS with ffmpeg + OpenCV 2.4.9. I’m working on a simple motion detection system which uses a stream from an IP camera (h264).
Once in a while the stream hiccups and throws in a "bad frame" (see pic-bad.png link below). The problem is, these frames vary largely from the previous frames and causes a "motion" event to get triggered even though no actual motion occured.
The pictures below will explain the problem.
Good frame (motion captured) :
Bad frame (no motion, just a broken frame) :
The bad frame gets caught randomly. I guess I can make a bad frame detector by analyzing (looping) through the pixels going down from a certain position to see if they are all the same, but I’m wondering if there is any other, more efficient, "by the book" approach to detecting these types of bad frames and just skipping over them.
Thank You !
EDIT UPDATE :
The frame is grabbed using a C++ motion detection program via
cvQueryFrame(camera);so I do not directly interface with ffmpeg, OpenCV does it on the backend. I’m using the latest version of ffmpeg compiled from git source. All of the libraries are also up to date (h264, etc, all downloaded and compiled yesterday). The data is coming from an RTSP stream (ffserver). I’ve tested over multiple cameras (dahua 1 - 3 MP models) and the frame glitch is pretty persistent across all of them, although it doesn’t happen continuously, just once on a while (ex : once every 10 minutes). -
What Is Data Misuse & How to Prevent It ? (With Examples)
13 mai 2024, par ErinYour data is everywhere. Every time you sign up for an email list, log in to Facebook or download a free app onto your smartphone, your data is being taken.
This can scare customers and users who fear their data will be misused.
While data can be a powerful asset for your business, it’s important you manage it well, or you could be in over your head.
In this guide, we break down what data misuse is, what the different types are, some examples of major data misuse and how you can prevent it so you can grow your brand sustainably.
What is data misuse ?
Data is a good thing.
It helps analysts and marketers understand their customers better so they can serve them relevant information, products and services to improve their lives.
But it can quickly become a bad thing for both the customers and business owners when it’s mishandled and misused.
Data misuse is when a business uses data outside of the agreed-upon terms. When companies collect data, they need to legally communicate how that data is being used.
Who or what determines when data is being misused ?
Several bodies :
- User agreements
- Data privacy laws
- Corporate policies
- Industry regulations
There are certain laws and regulations around how you can collect and use data. Failure to comply with these guidelines and rules can result in several consequences, including legal action.
Keep reading to discover the different types of data misuse and how to prevent it.
3 types of data misuse
There are a few different types of data misuse.
If you fail to understand them, you could face penalties, legal trouble and a poor brand reputation.
1. Commingling
When you collect data, you need to ensure you’re using it for the right purpose. Commingling is when an organisation collects data from a specific audience for a specific reason but then uses the data for another purpose.
One example of commingling is if a company shares sensitive customer data with another company. In many cases, sister companies will share data even if the terms of the data collection didn’t include that clause.
Another example is if someone collects data for academic purposes like research but then uses the data later on for marketing purposes to drive business growth in a for-profit company.
In either case, the company went wrong by not being clear on what the data would be used for. You must communicate with your audience exactly how the data will be used.
2. Personal benefit
The second common way data is misused in the workplace is through “personal benefit.” This is when someone with access to data abuses it for their own gain.
The most common example of personal benefit data muse is when an employee misuses internal data.
While this may sound like each instance of data misuse is caused by malicious intent, that’s not always the case. Data misuse can still exist even if an employee didn’t have any harmful intent behind their actions.
One of the most common examples is when an employee mistakenly moves data from a company device to personal devices for easier access.
3. Ambiguity
As mentioned above, when discussing commingling, a company must only use data how they say they will use it when they collect it.
A company can misuse data when they’re unclear on how the data is used. Ambiguity is when a company fails to disclose how user data is being collected and used.
This means communicating poorly on how the data will be used can be wrong and lead to misuse.
One of the most common ways this happens is when a company doesn’t know how to use the data, so they can’t give a specific reason. However, this is still considered misuse, as companies need to disclose exactly how they will use the data they collect from their customers.
Laws on data misuse you need to follow
Data misuse can lead to poor reputations and penalties from big tech companies. For example, if you step outside social media platforms’ guidelines, you could be suspended, banned or shadowbanned.
But what’s even more important is certain types of data misuse could mean you’re breaking laws worldwide. Here are some laws on data misuse you need to follow to avoid legal trouble :
General Data Protection Regulation (GDPR)
The GDPR, or General Data Protection Regulation, is a law within the European Union (EU) that went into effect in 2018.
The GDPR was implemented to set a standard and improve data protection in Europe. It was also established to increase accountability and transparency for data breaches within businesses and organisations.
The purpose of the GDPR is to protect residents within the European Union.
The penalties for breaking GDPR laws are fines up to 20 million Euros or 4% of global revenues (whatever the higher amount is).
The GDPR doesn’t just affect companies in Europe. You can break the GDPR’s laws regardless of where your organisation is located worldwide. As long as your company collects, processes or uses the personal data of any EU resident, you’re subject to the GDPR’s rules.
If you want to track user data to grow your business, you need to ensure you’re following international data laws. Tools like Matomo—the world’s leading privacy-friendly web analytics solution—can help you achieve GDPR compliance and maintain it.
With Matomo, you can confidently enhance your website’s performance, knowing that you’re adhering to data protection laws.
Try Matomo for Free
Get the web insights you need, without compromising data accuracy.
California Consumer Privacy Act (CCPA)
The California Consumer Privacy Act (CCPA) is another important data law companies worldwide must follow.
Like GDPR, the CCPA is a data privacy law established to protect residents of a certain region — in this case, residents of California in the United States.
The CCPA was implemented in 2020, and businesses worldwide can be penalised for breaking the regulations. For example, if you’re found violating the CCPA, you could be fined $7,500 for each intentional violation.
If you have unintentional violations, you could still be fined, but at a lesser fee of $2,500.
The Gramm-Leach-Bliley Act (GLBA)
If your business is located within the United States, then you’re subject to a federal law implemented in 1999 called The Gramm-Leach-Bliley Act (GLB Act or GLBA).
The GLBA is also known as the Financial Modernization Act of 1999. Its purpose is to control the way American financial institutions handle consumer data.
In the GLBA, there are three sections :
- The Financial Privacy Rule : regulates the collection and disclosure of private financial data.
- Safeguards Rule : Financial institutions must establish security programs to protect financial data.
- Pretexting Provisions : Prohibits accessing private data using false pretences.
The GLBA also requires financial institutions in the U.S. to give their customers written privacy policy communications that explain their data-sharing practices.
4 examples of data misuse in real life
If you want to see what data misuse looks like in real life, look no further.
Big tech is central to some of the biggest data misuses and scandals.
Here are a few examples of data misuse in real life you should take note of to avoid a similar scenario :
1. Facebook election interference
One of history’s most famous examples of data misuse is the Facebook and Cambridge Analytica scandal in 2018.
During the 2018 U.S. midterm elections, Cambridge Analytica, a political consulting firm, acquired personal data from Facebook users that was said to have been collected for academic research.
Instead, Cambridge Analytica used data from roughly 87 million Facebook users.
This is a prime example of commingling.
The result ? Cambridge Analytica was left bankrupt and dissolved, and Facebook was fined $5 billion by the Federal Trade Commission (FTC).
2. Uber “God View” tracking
Another big tech company, Uber, was caught misusing data a decade ago.
Why ?
Uber implemented a new feature for its employees in 2014 called “God View.”
The tool enabled Uber employees to track riders using their app. The problem was that they were watching them without the users’ permission. “God View” lets Uber spy on their riders to see their movements and locations.
The FTC ended up slapping them with a major lawsuit, and as part of their settlement agreement, Uber agreed to have an outside firm audit their privacy practices between 2014 and 2034.
3. Twitter targeted ads overstep
In 2019, Twitter was found guilty of allowing advertisers to access its users’ personal data to improve advertisement targeting.
Advertisers were given access to user email addresses and phone numbers without explicit permission from the users. The result was that Twitter ad buyers could use this contact information to cross-reference with Twitter’s data to serve ads to them.
Twitter stated that the data leak was an internal error.
4. Google location tracking
In 2020, Google was found guilty of not explicitly disclosing how it’s using its users’ personal data, which is an example of ambiguity.
The result ?
The French data protection authority fined Google $57 million.
8 ways to prevent data misuse in your company
Now that you know the dangers of data misuse and its associated penalties, it’s time to understand how you can prevent it in your company.
Here are eight ways you can prevent data misuse :
1. Track data with an ethical web analytics solution
You can’t get by in today’s business world without tracking data. The question is whether you’re tracking it safely or not.
If you want to ensure you aren’t getting into legal trouble with data misuse, then you need to use an ethical web analytics solution like Matomo.
With it, you can track and improve your website performance while remaining GDPR-compliant and respecting user privacy. Unlike other web analytics solutions that monetise your data and auction it off to advertisers, with Matomo, you own your data.
Try Matomo for Free
Get the web insights you need, without compromising data accuracy.
2. Don’t share data with big tech
As the data misuse examples above show, big tech companies often violate data privacy laws.
And while most of these companies, like Google, appear to be convenient, they’re often inconvenient (and much worse), especially regarding data leaks, privacy breaches and the sale of your data to advertisers.
Have you ever heard the phrase : “You are the product ?” When it comes to big tech, chances are if you’re getting it for free, you (and your data) are the products they’re selling.
The best way to stop sharing data with big tech is to stop using platforms like Google. For more ideas on different Google product alternatives, check out this list of Google alternatives.
3. Identity verification
Data misuse typically isn’t a company-wide ploy. Often, it’s the lack of security structure and systems within your company.
An important place to start is to ensure proper identity verification for anyone with access to your data.
4. Access management
After establishing identity verification, you should ensure you have proper access management set up. For example, you should only give specific access to specific roles in your company to prevent data misuse.
5. Activity logs and monitoring
One way to track data misuse or breaches is by setting up activity logs to ensure you can see who is accessing certain types of data and when they’re accessing it.
You should ensure you have a team dedicated to continuously monitoring these logs to catch anything quickly.
6. Behaviour alerts
While manually monitoring data is important, it’s also good to set up automatic alerts if there is unusual activity around your data centres. You should set up behaviour alerts and notifications in case threats or compromising events occur.
7. Onboarding, training, education
One way to ensure quality data management is to keep your employees up to speed on data security. You should ensure data security is a part of your employee onboarding. Also, you should have regular training and education to keep people informed on protecting company and customer data.
8. Create data protocols and processes
To ensure long-term data security, you should establish data protocols and processes.
To protect your user data, set up rules and systems within your organisation that people can reference and follow continuously to prevent data misuse.
Leverage data ethically with Matomo
Data is everything in business.
But it’s not something to be taken lightly. Mishandling user data can break customer trust, lead to penalties from organisations and even create legal trouble and massive fines.
You should only use privacy-first tools to ensure you’re handling data responsibly.
Matomo is a privacy-friendly web analytics tool that collects, stores and tracks data across your website without breaking privacy laws.
With over 1 million websites using Matomo, you can track and improve website performance with :
- Accurate data (no data sampling)
- Privacy-friendly and compliant with privacy regulations like GDPR, CCPA and more
- Advanced features like heatmaps, session recordings, A/B testing and more
Try Matomo free for 21-days. No credit card required.
Try Matomo for Free
21 day free trial. No credit card required.
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avcodec/x86/vvc : add alf filter luma and chroma avx2 optimizations
13 mai 2024, par Wu Jianhuaavcodec/x86/vvc : add alf filter luma and chroma avx2 optimizations
ff_vvc_alf_filter_luma_4x4_10_c : 135
ff_vvc_alf_filter_luma_4x4_10_avx2 : 54
ff_vvc_alf_filter_luma_4x8_10_c : 268
ff_vvc_alf_filter_luma_4x8_10_avx2 : 106
ff_vvc_alf_filter_luma_4x12_10_c : 400
ff_vvc_alf_filter_luma_4x12_10_avx2 : 160
ff_vvc_alf_filter_luma_4x16_10_c : 535
ff_vvc_alf_filter_luma_4x16_10_avx2 : 213
ff_vvc_alf_filter_luma_4x20_10_c : 646
ff_vvc_alf_filter_luma_4x20_10_avx2 : 262
ff_vvc_alf_filter_luma_4x24_10_c : 783
ff_vvc_alf_filter_luma_4x24_10_avx2 : 309
ff_vvc_alf_filter_luma_4x28_10_c : 908
ff_vvc_alf_filter_luma_4x28_10_avx2 : 361
ff_vvc_alf_filter_luma_4x32_10_c : 1039
ff_vvc_alf_filter_luma_4x32_10_avx2 : 412
ff_vvc_alf_filter_luma_8x4_10_c : 260
ff_vvc_alf_filter_luma_8x4_10_avx2 : 53
ff_vvc_alf_filter_luma_8x8_10_c : 516
ff_vvc_alf_filter_luma_8x8_10_avx2 : 105
ff_vvc_alf_filter_luma_8x12_10_c : 779
ff_vvc_alf_filter_luma_8x12_10_avx2 : 157
ff_vvc_alf_filter_luma_8x16_10_c : 1038
ff_vvc_alf_filter_luma_8x16_10_avx2 : 210
ff_vvc_alf_filter_luma_8x20_10_c : 1293
ff_vvc_alf_filter_luma_8x20_10_avx2 : 259
ff_vvc_alf_filter_luma_8x24_10_c : 1553
ff_vvc_alf_filter_luma_8x24_10_avx2 : 309
ff_vvc_alf_filter_luma_8x28_10_c : 1815
ff_vvc_alf_filter_luma_8x28_10_avx2 : 361
ff_vvc_alf_filter_luma_8x32_10_c : 2067
ff_vvc_alf_filter_luma_8x32_10_avx2 : 419
ff_vvc_alf_filter_luma_12x4_10_c : 390
ff_vvc_alf_filter_luma_12x4_10_avx2 : 54
ff_vvc_alf_filter_luma_12x8_10_c : 773
ff_vvc_alf_filter_luma_12x8_10_avx2 : 107
ff_vvc_alf_filter_luma_12x12_10_c : 1159
ff_vvc_alf_filter_luma_12x12_10_avx2 : 155
ff_vvc_alf_filter_luma_12x16_10_c : 1550
ff_vvc_alf_filter_luma_12x16_10_avx2 : 207
ff_vvc_alf_filter_luma_12x20_10_c : 1970
ff_vvc_alf_filter_luma_12x20_10_avx2 : 260
ff_vvc_alf_filter_luma_12x24_10_c : 2379
ff_vvc_alf_filter_luma_12x24_10_avx2 : 309
ff_vvc_alf_filter_luma_12x28_10_c : 2763
ff_vvc_alf_filter_luma_12x28_10_avx2 : 362
ff_vvc_alf_filter_luma_12x32_10_c : 3158
ff_vvc_alf_filter_luma_12x32_10_avx2 : 419
ff_vvc_alf_filter_luma_16x4_10_c : 523
ff_vvc_alf_filter_luma_16x4_10_avx2 : 53
ff_vvc_alf_filter_luma_16x8_10_c : 1049
ff_vvc_alf_filter_luma_16x8_10_avx2 : 103
ff_vvc_alf_filter_luma_16x12_10_c : 1566
ff_vvc_alf_filter_luma_16x12_10_avx2 : 159
ff_vvc_alf_filter_luma_16x16_10_c : 2078
ff_vvc_alf_filter_luma_16x16_10_avx2 : 211
ff_vvc_alf_filter_luma_16x20_10_c : 2631
ff_vvc_alf_filter_luma_16x20_10_avx2 : 259
ff_vvc_alf_filter_luma_16x24_10_c : 3149
ff_vvc_alf_filter_luma_16x24_10_avx2 : 316
ff_vvc_alf_filter_luma_16x28_10_c : 3631
ff_vvc_alf_filter_luma_16x28_10_avx2 : 359
ff_vvc_alf_filter_luma_16x32_10_c : 4233
ff_vvc_alf_filter_luma_16x32_10_avx2 : 428
ff_vvc_alf_filter_luma_20x4_10_c : 649
ff_vvc_alf_filter_luma_20x4_10_avx2 : 106
ff_vvc_alf_filter_luma_20x8_10_c : 1294
ff_vvc_alf_filter_luma_20x8_10_avx2 : 206
ff_vvc_alf_filter_luma_20x12_10_c : 1936
ff_vvc_alf_filter_luma_20x12_10_avx2 : 310
ff_vvc_alf_filter_luma_20x16_10_c : 2594
ff_vvc_alf_filter_luma_20x16_10_avx2 : 411
ff_vvc_alf_filter_luma_20x20_10_c : 3234
ff_vvc_alf_filter_luma_20x20_10_avx2 : 517
ff_vvc_alf_filter_luma_20x24_10_c : 3894
ff_vvc_alf_filter_luma_20x24_10_avx2 : 621
ff_vvc_alf_filter_luma_20x28_10_c : 4542
ff_vvc_alf_filter_luma_20x28_10_avx2 : 722
ff_vvc_alf_filter_luma_20x32_10_c : 5205
ff_vvc_alf_filter_luma_20x32_10_avx2 : 832
ff_vvc_alf_filter_luma_24x4_10_c : 774
ff_vvc_alf_filter_luma_24x4_10_avx2 : 104
ff_vvc_alf_filter_luma_24x8_10_c : 1546
ff_vvc_alf_filter_luma_24x8_10_avx2 : 206
ff_vvc_alf_filter_luma_24x12_10_c : 2318
ff_vvc_alf_filter_luma_24x12_10_avx2 : 312
ff_vvc_alf_filter_luma_24x16_10_c : 3104
ff_vvc_alf_filter_luma_24x16_10_avx2 : 411
ff_vvc_alf_filter_luma_24x20_10_c : 3893
ff_vvc_alf_filter_luma_24x20_10_avx2 : 513
ff_vvc_alf_filter_luma_24x24_10_c : 4681
ff_vvc_alf_filter_luma_24x24_10_avx2 : 616
ff_vvc_alf_filter_luma_24x28_10_c : 5474
ff_vvc_alf_filter_luma_24x28_10_avx2 : 721
ff_vvc_alf_filter_luma_24x32_10_c : 6271
ff_vvc_alf_filter_luma_24x32_10_avx2 : 832
ff_vvc_alf_filter_luma_28x4_10_c : 907
ff_vvc_alf_filter_luma_28x4_10_avx2 : 103
ff_vvc_alf_filter_luma_28x8_10_c : 1797
ff_vvc_alf_filter_luma_28x8_10_avx2 : 206
ff_vvc_alf_filter_luma_28x12_10_c : 2708
ff_vvc_alf_filter_luma_28x12_10_avx2 : 309
ff_vvc_alf_filter_luma_28x16_10_c : 3632
ff_vvc_alf_filter_luma_28x16_10_avx2 : 413
ff_vvc_alf_filter_luma_28x20_10_c : 4537
ff_vvc_alf_filter_luma_28x20_10_avx2 : 519
ff_vvc_alf_filter_luma_28x24_10_c : 5463
ff_vvc_alf_filter_luma_28x24_10_avx2 : 616
ff_vvc_alf_filter_luma_28x28_10_c : 6372
ff_vvc_alf_filter_luma_28x28_10_avx2 : 719
ff_vvc_alf_filter_luma_28x32_10_c : 7274
ff_vvc_alf_filter_luma_28x32_10_avx2 : 823
ff_vvc_alf_filter_luma_32x4_10_c : 1029
ff_vvc_alf_filter_luma_32x4_10_avx2 : 104
ff_vvc_alf_filter_luma_32x8_10_c : 2060
ff_vvc_alf_filter_luma_32x8_10_avx2 : 206
ff_vvc_alf_filter_luma_32x12_10_c : 3112
ff_vvc_alf_filter_luma_32x12_10_avx2 : 307
ff_vvc_alf_filter_luma_32x16_10_c : 4161
ff_vvc_alf_filter_luma_32x16_10_avx2 : 413
ff_vvc_alf_filter_luma_32x20_10_c : 5211
ff_vvc_alf_filter_luma_32x20_10_avx2 : 514
ff_vvc_alf_filter_luma_32x24_10_c : 6238
ff_vvc_alf_filter_luma_32x24_10_avx2 : 614
ff_vvc_alf_filter_luma_32x28_10_c : 7261
ff_vvc_alf_filter_luma_32x28_10_avx2 : 720
ff_vvc_alf_filter_luma_32x32_10_c : 8312
ff_vvc_alf_filter_luma_32x32_10_avx2 : 819
ff_vvc_alf_filter_chroma_4x4_10_c : 70
ff_vvc_alf_filter_chroma_4x4_10_avx2 : 53
ff_vvc_alf_filter_chroma_4x8_10_c : 139
ff_vvc_alf_filter_chroma_4x8_10_avx2 : 104
ff_vvc_alf_filter_chroma_4x12_10_c : 208
ff_vvc_alf_filter_chroma_4x12_10_avx2 : 155
ff_vvc_alf_filter_chroma_4x16_10_c : 275
ff_vvc_alf_filter_chroma_4x16_10_avx2 : 218
ff_vvc_alf_filter_chroma_4x20_10_c : 344
ff_vvc_alf_filter_chroma_4x20_10_avx2 : 257
ff_vvc_alf_filter_chroma_4x24_10_c : 411
ff_vvc_alf_filter_chroma_4x24_10_avx2 : 309
ff_vvc_alf_filter_chroma_4x28_10_c : 481
ff_vvc_alf_filter_chroma_4x28_10_avx2 : 361
ff_vvc_alf_filter_chroma_4x32_10_c : 545
ff_vvc_alf_filter_chroma_4x32_10_avx2 : 411
ff_vvc_alf_filter_chroma_8x4_10_c : 138
ff_vvc_alf_filter_chroma_8x4_10_avx2 : 53
ff_vvc_alf_filter_chroma_8x8_10_c : 274
ff_vvc_alf_filter_chroma_8x8_10_avx2 : 106
ff_vvc_alf_filter_chroma_8x12_10_c : 422
ff_vvc_alf_filter_chroma_8x12_10_avx2 : 158
ff_vvc_alf_filter_chroma_8x16_10_c : 545
ff_vvc_alf_filter_chroma_8x16_10_avx2 : 206
ff_vvc_alf_filter_chroma_8x20_10_c : 683
ff_vvc_alf_filter_chroma_8x20_10_avx2 : 257
ff_vvc_alf_filter_chroma_8x24_10_c : 816
ff_vvc_alf_filter_chroma_8x24_10_avx2 : 312
ff_vvc_alf_filter_chroma_8x28_10_c : 951
ff_vvc_alf_filter_chroma_8x28_10_avx2 : 359
ff_vvc_alf_filter_chroma_8x32_10_c : 1098
ff_vvc_alf_filter_chroma_8x32_10_avx2 : 409
ff_vvc_alf_filter_chroma_12x4_10_c : 204
ff_vvc_alf_filter_chroma_12x4_10_avx2 : 53
ff_vvc_alf_filter_chroma_12x8_10_c : 410
ff_vvc_alf_filter_chroma_12x8_10_avx2 : 104
ff_vvc_alf_filter_chroma_12x12_10_c : 614
ff_vvc_alf_filter_chroma_12x12_10_avx2 : 155
ff_vvc_alf_filter_chroma_12x16_10_c : 814
ff_vvc_alf_filter_chroma_12x16_10_avx2 : 210
ff_vvc_alf_filter_chroma_12x20_10_c : 1017
ff_vvc_alf_filter_chroma_12x20_10_avx2 : 258
ff_vvc_alf_filter_chroma_12x24_10_c : 1221
ff_vvc_alf_filter_chroma_12x24_10_avx2 : 308
ff_vvc_alf_filter_chroma_12x28_10_c : 1423
ff_vvc_alf_filter_chroma_12x28_10_avx2 : 366
ff_vvc_alf_filter_chroma_12x32_10_c : 1624
ff_vvc_alf_filter_chroma_12x32_10_avx2 : 410
ff_vvc_alf_filter_chroma_16x4_10_c : 272
ff_vvc_alf_filter_chroma_16x4_10_avx2 : 52
ff_vvc_alf_filter_chroma_16x8_10_c : 541
ff_vvc_alf_filter_chroma_16x8_10_avx2 : 105
ff_vvc_alf_filter_chroma_16x12_10_c : 812
ff_vvc_alf_filter_chroma_16x12_10_avx2 : 155
ff_vvc_alf_filter_chroma_16x16_10_c : 1091
ff_vvc_alf_filter_chroma_16x16_10_avx2 : 206
ff_vvc_alf_filter_chroma_16x20_10_c : 1354
ff_vvc_alf_filter_chroma_16x20_10_avx2 : 257
ff_vvc_alf_filter_chroma_16x24_10_c : 1637
ff_vvc_alf_filter_chroma_16x24_10_avx2 : 313
ff_vvc_alf_filter_chroma_16x28_10_c : 1899
ff_vvc_alf_filter_chroma_16x28_10_avx2 : 359
ff_vvc_alf_filter_chroma_16x32_10_c : 2161
ff_vvc_alf_filter_chroma_16x32_10_avx2 : 410
ff_vvc_alf_filter_chroma_20x4_10_c : 339
ff_vvc_alf_filter_chroma_20x4_10_avx2 : 103
ff_vvc_alf_filter_chroma_20x8_10_c : 681
ff_vvc_alf_filter_chroma_20x8_10_avx2 : 207
ff_vvc_alf_filter_chroma_20x12_10_c : 1013
ff_vvc_alf_filter_chroma_20x12_10_avx2 : 307
ff_vvc_alf_filter_chroma_20x16_10_c : 1349
ff_vvc_alf_filter_chroma_20x16_10_avx2 : 415
ff_vvc_alf_filter_chroma_20x20_10_c : 1685
ff_vvc_alf_filter_chroma_20x20_10_avx2 : 522
ff_vvc_alf_filter_chroma_20x24_10_c : 2037
ff_vvc_alf_filter_chroma_20x24_10_avx2 : 622
ff_vvc_alf_filter_chroma_20x28_10_c : 2380
ff_vvc_alf_filter_chroma_20x28_10_avx2 : 733
ff_vvc_alf_filter_chroma_20x32_10_c : 2712
ff_vvc_alf_filter_chroma_20x32_10_avx2 : 838
ff_vvc_alf_filter_chroma_24x4_10_c : 408
ff_vvc_alf_filter_chroma_24x4_10_avx2 : 104
ff_vvc_alf_filter_chroma_24x8_10_c : 818
ff_vvc_alf_filter_chroma_24x8_10_avx2 : 207
ff_vvc_alf_filter_chroma_24x12_10_c : 1219
ff_vvc_alf_filter_chroma_24x12_10_avx2 : 308
ff_vvc_alf_filter_chroma_24x16_10_c : 1648
ff_vvc_alf_filter_chroma_24x16_10_avx2 : 420
ff_vvc_alf_filter_chroma_24x20_10_c : 2061
ff_vvc_alf_filter_chroma_24x20_10_avx2 : 525
ff_vvc_alf_filter_chroma_24x24_10_c : 2437
ff_vvc_alf_filter_chroma_24x24_10_avx2 : 617
ff_vvc_alf_filter_chroma_24x28_10_c : 2832
ff_vvc_alf_filter_chroma_24x28_10_avx2 : 722
ff_vvc_alf_filter_chroma_24x32_10_c : 3271
ff_vvc_alf_filter_chroma_24x32_10_avx2 : 830
ff_vvc_alf_filter_chroma_28x4_10_c : 476
ff_vvc_alf_filter_chroma_28x4_10_avx2 : 104
ff_vvc_alf_filter_chroma_28x8_10_c : 948
ff_vvc_alf_filter_chroma_28x8_10_avx2 : 205
ff_vvc_alf_filter_chroma_28x12_10_c : 1420
ff_vvc_alf_filter_chroma_28x12_10_avx2 : 310
ff_vvc_alf_filter_chroma_28x16_10_c : 1889
ff_vvc_alf_filter_chroma_28x16_10_avx2 : 423
ff_vvc_alf_filter_chroma_28x20_10_c : 2372
ff_vvc_alf_filter_chroma_28x20_10_avx2 : 513
ff_vvc_alf_filter_chroma_28x24_10_c : 2843
ff_vvc_alf_filter_chroma_28x24_10_avx2 : 618
ff_vvc_alf_filter_chroma_28x28_10_c : 3307
ff_vvc_alf_filter_chroma_28x28_10_avx2 : 724
ff_vvc_alf_filter_chroma_28x32_10_c : 3801
ff_vvc_alf_filter_chroma_28x32_10_avx2 : 827
ff_vvc_alf_filter_chroma_32x4_10_c : 543
ff_vvc_alf_filter_chroma_32x4_10_avx2 : 105
ff_vvc_alf_filter_chroma_32x8_10_c : 1084
ff_vvc_alf_filter_chroma_32x8_10_avx2 : 206
ff_vvc_alf_filter_chroma_32x12_10_c : 1621
ff_vvc_alf_filter_chroma_32x12_10_avx2 : 309
ff_vvc_alf_filter_chroma_32x16_10_c : 2173
ff_vvc_alf_filter_chroma_32x16_10_avx2 : 408
ff_vvc_alf_filter_chroma_32x20_10_c : 2703
ff_vvc_alf_filter_chroma_32x20_10_avx2 : 513
ff_vvc_alf_filter_chroma_32x24_10_c : 3245
ff_vvc_alf_filter_chroma_32x24_10_avx2 : 612
ff_vvc_alf_filter_chroma_32x28_10_c : 3795
ff_vvc_alf_filter_chroma_32x28_10_avx2 : 722
ff_vvc_alf_filter_chroma_32x32_10_c : 4339
ff_vvc_alf_filter_chroma_32x32_10_avx2 : 820Signed-off-by : Wu Jianhua <toqsxw@outlook.com>
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