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• What Is Ethical SEO & Why Does It Matter ?

  7 mai 2024, par Erin

  Do you want to generate more revenue ?

  Then, you need to ensure you have a steady stream of traffic flowing to your site.

  Search engines like Google, Bing and Yahoo are powerful mediums you can use to scale your business.

  Search engine optimisation (SEO) is the process of creating search engine-friendly content to draw in traffic to your website. But, if you aren’t careful, you could be crossing the line of ethical SEO into unethical SEO.

  In this article, we break down what ethical SEO is, why it’s important in business and how you can implement effective SEO into your business while remaining ethical.

  Let’s begin.

  What is ethical SEO ?

  Since the early days of the internet and search engines, business owners and marketers have tried using all kinds of SEO tactics to rank atop the search engines for relevant keywords.

  The problem ?

  Some of these practices are ethical, while others aren’t.

  What exactly is ethical SEO ?

  It’s the practice of optimising your website’s rankings in search engines by following search engine guidelines and prioritising user experience.

  

  Ethical SEO is also referred to as “white hat SEO.”

  On the other hand, businesses that break search engine rules and guidelines to “hack” their way to the top with faulty and questionable practices use unethical SEO, or “black hat SEO.”

  Ethical SEO aims to achieve higher rankings in search engines through sustainable, legitimate and fair methods.

  Black hat, or unethical SEO, aims to manipulate or “game” the system with deceptive strategies to bypass the search engine’s guidelines to rank higher.

  The two core branches of ethical SEO include :

  1. Strategies that align with search engine guidelines.
  2. Accessibility to broad audiences.

  Some examples of ethical SEO principles include :

  • Natural link building
  • Compliance with search engine guidelines
  • Establishing great user experiences
  • Creating reader-focused content

  By sticking to the right guidelines and implementing proper SEO practices, businesses can establish ethical SEO to generate more traffic and grow their brands.

  8 ethical SEO practices to implement

  If you want to grow your organic search traffic, then there’s no doubt you’ll need to have some SEO knowledge.

  While there are dozens of ways to “game” SEO, it’s best to stick to proven, ethical SEO techniques to improve your rankings.

  Stick to these best practices to increase your rankings in the search engine results pages (SERPs), increase organic traffic and improve your website conversions.

  

  1. Crafting high-quality content

  The most important piece of any ethical SEO strategy is content.

  Forget about rankings, keywords and links for a second.

  Step back and think about why people go to Google, Bing and Yahoo in the first place.

  They’re there looking for information. They have a question they need answered. That’s where you can come in and give them the answer they want. 

  How ? In the form of content.

  The best long-term ethical SEO strategy is to create the highest-quality content possible. Crafting high-quality content should be where you focus 90% of your SEO efforts.

  2. Following search engine guidelines

  Once you’ve got a solid content creation strategy, where you’re producing in-depth, quality content, you need to ensure you’re following the guidelines and rules put in place by the major search engines.

  This means you need to stay compliant with the best practices and guidelines laid out by the top search engines.

  If you fail to follow these rules, you could be penalised, your content could be downgraded or removed from search engines, and you could even have your entire website flagged, impacting your entire organic search traffic from your site.

  You need to ensure you align with the guidelines so you’re set up for long-term success with your SEO.

  3. Conducting keyword research and optimisation

  Now that we’ve covered content and guidelines, let’s talk about the technical stuff, starting with keywords.

  In the early days of SEO (late 90s), just about anyone could rank a web page high by stuffing keywords all over the page.

  While those black hat techniques used to work to “game” the system, it doesn’t work like that anymore. Google and other major search engines have much more advanced algorithms that can detect keyword stuffing and manipulation.

  Keywords are still a major part of a successful SEO strategy. You can ethically incorporate keywords into your content (and you should) if you want to rank higher. 

  Your main goal with your content is to match it with the search intent. So, incorporating keywords should come naturally throughout your content. If you try to stuff in unnecessary keywords or use spammy techniques, you may not even rank at all and could harm your website’s rankings.

  4. Incorporating natural link building

  After you’ve covered content and keywords, it’s time to dive into links. Backlinks are any links that point back to your website from another website.

  These are a crucial part of the SEO pie. Without them, it’s hard to rank high on Google. They work well because they tell Google your web page or website has authority on a subject matter.

  But you could be penalised if you try to manipulate backlinks by purchasing them or spamming them from other websites.

  Instead, you should aim to draw in natural backlinks by creating content that attracts them.

  How ? There are several options :

  • Content marketing
  • Email outreach
  • Brand mentions
  • Public relations
  • Ethical guest posting

  Get involved in other people’s communities. Get on podcasts. Write guest posts. Connect with other brands. Provide value in your niche and create content worth linking to.

  5. Respecting the intellectual property of other brands

  Content creation is moving at lightspeed in the creator economy and social media era. For better or for worse, content is going viral every day. People share content, place their spin on it, revise it, optimise it, and spread it around the internet.

  Unfortunately, this means the content is sometimes shared without the owner’s permission. Content is one form of intellectual property (IP). 

  If you share copyrighted material, you could face legal consequences.

  6. Ensuring transparency

  Transparency is one of the pillars of ethical marketing.

  If you’re running the SEO in your company or an agency, you should always explain the SEO strategies and tactics you’re implementing to your stakeholders.

  It’s best to lean on transparency and honesty to ensure your team knows you’re running operations ethically.

  7. Implementing a great user experience

  The final pillar of ethical SEO practices is offering a great user experience on your website.

  Major search engines like Google are favouring user experience more and more every year. This means knowing how to track and analyse website metrics like page load times, time on page, pageviews, media plays and event tracking.

  8. Use an ethical web analytics solution

  Last but certainly not least. Tracking your website visitors ethically is key to maintaining SEO ethics.

  You can do this by using an ethical web analytics solution like Matomo, Plausible or Fathom. All three are committed to respecting user privacy and offer ethical tracking of visitors.

  We’re a bit biassed towards Matomo, of course, but for good reasons.

  Matomo offers accurate, unsampled data along with advanced features like heatmaps, session recording, and A/B testing. These features enhance user experience and support ethical SEO practices by providing insights into user behaviour, helping optimise content. 

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  6 unethical SEO practices to avoid

  Now that we’ve covered the ethical SEO best practices let’s talk about what kind of unethical SEO practices you want to avoid.

  Remember, SEO isn’t as easy to manipulate as it once was 20 years ago.

  Algorithms are much more sophisticated now, and search engines are getting better at detecting fraudulent, scammy or unethical SEO practices every year.

  Avoid these eight unethical SEO practices to ensure you can rank high in the long term :

  

  1. Keyword stuffing

  Keyword stuffing is probably the most common unethical SEO practice. This is where someone deliberately stuffs keywords onto a page to manipulate the search engines to rank a web page higher.

  Where this is unethical isn’t always easy to detect, but in some cases, it is. It comes down to whether it’s relevant and natural or intentionally stuffing.

  2. Cloaking

  Cloaking is another unethical SEO practice where someone manipulates the information search engines see on their website.

  For example, someone may show search engines one web page on their website, but when someone clicks on it in Google, they can direct someone to a completely different page. They do this by detecting the incoming request from the user agent and presenting different content.

  3. Deceiving functionality

  Another way companies are unethically implementing SEO tactics is by deceiving people with misleading information. For example, a website may claim to provide a free resource or directory but may intentionally lead visitors to paid products.

  4. Fraudulent redirects

  Another way to deceive or mislead searchers is by creating fraudulent redirects. A redirect is a way to take someone to a different web page when they click on another one. Redirects can be useful if a page is broken or outdated. However, they can be used to deceptively take someone to a website they didn’t intend to view.

  5. Negative SEO

  Negative SEO is the intentional attempt to harm a competitor’s search engine rankings through unethical tactics.

  These tactics include duplicating their content or generating spammy links by creating low quality or irrelevant backlinks to their site.

  6. Hidden text

  Placing hidden text on a website typically has one purpose : keyword stuffing.

  Instead of making it visible to users reading the content, websites will place invisible text or text that’s hard to read on a website to try to rank the content higher and manipulate the search engines.

  3 reasons you need to implement ethical SEO

  So, why should you ensure you only implement ethical SEO in your organic traffic strategy ?

  It’s not just about what’s morally right or wrong. Implementing ethical SEO is the smartest long-term marketing strategy :

  1. Better long-term SEO

  Search engine optimisation is about implementing the “right” tactics to get your website to rank higher.

  The funny thing is many people are trying to get quick fixes by manipulating search engines to see results now.

  However, the ones who implement shady tactics and “hacks” to game the system almost always end up losing their rankings in the long term. 

  The best long-term SEO strategy is to do things ethically. Create content that helps people. Make higher quality content than your competitors. If you do those two things right, you’ll have better search traffic for years.

  2. Great brand reputation

  Not only is ethical SEO a great way to get long-term results, but it’s also a good way to maintain a solid brand reputation.

  Reputation management is a crucial aspect of SEO. All it takes is one bad incident, and your SEO could be negatively impacted.

  3. Lower chance of penalties

  If you play by the rules, you have a lower risk of being penalised by Google.

  The reality is that Google owns the search engine, not you. While we can benefit from the traffic generation of major search engines, you could lose all your rankings if you break their guidelines.

  Track SEO data ethically with Matomo

  Ethical SEO is all about :

  • Serving your audience
  • Getting better traffic in the long run

  If you fail to follow ethical SEO practices, you could be de-ranked or have your reputation on the line.

  However, if you implement ethical SEO, you could reap the rewards of a sustainable marketing strategy that helps you grow your traffic correctly and increase conversions in the long term.

  If you’re ready to start implementing ethical SEO, you need to ensure you depend on an ethical web analytics solution like Matomo.

  Unlike other web analytics solutions, Matomo prioritises user privacy, maintains transparent, ethical data collection practices, and does not sell user data to advertisers. Matomo provides 100% data ownership, ensuring that your data remains yours to own and control.

  As the leading privacy-friendly web analytics solution globally, trusted by over 1 million websites, Matomo ensures :

  • Accurate data without data sampling for confident insights and better results
  • Privacy-friendly and GDPR-compliant web analytics
  • Open-source access for transparency and creating a custom solution tailored to your needs

  Try Matomo free for 21-days. No credit card required.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I accept the terms and conditions and data processing agreement (DPA)

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  
  

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• How to Implement Cross-Channel Analytics : A Guide for Marketers

  17 avril 2024, par Erin

  Every modern marketer knows they have to connect with consumers across several channels. But do you know how well Instagram works alongside organic traffic or your email list ? Are you even tracking the impacts of these channels in one place ?

  You need a cross-channel analytics solution if you answered no to either of these questions. 

  In this article, we’ll explain cross-channel analytics, why your company probably needs it and how to set up a cross-channel analytics solution as quickly and easily as possible.

  What is cross-channel analytics ? 

  Cross-channel analytics is a form of marketing analytics that collects and analyses data from every channel and campaign you use.

  The result is a comprehensive view of your customer’s journey and each channel’s role in converting customers. 

  Cross-channel analytics lets you track every channel you use to convert customers, including :

  • Your website
  • Social media profiles
  • Email
  • Paid search
  • E-commerce
  • Retargeting campaigns

  Cross-channel analytics solves one of the most significant issues of cross-channel or multi-channel marketing efforts : measurement. 

  Research shows that only 16% of marketing tech stacks allow for accurate measurement of multi-channel initiatives across channels. 

  That’s a problem, given the staggering number of touchpoints in a typical buyer’s conversion path. However, it can be fixed using a cross-channel analytics approach that lets you measure the performance of every channel and assign a dollar value to its role in every conversion. 

  The difference between cross-channel analytics and multi-channel analytics

  Cross-channel analytics and multi-channel analytics sound very similar, but there’s one key difference you need to know. Multi-channel analytics measures the performance of several channels, but not necessarily all of them, nor the extent to which they work together to drive conversions. Conversely, cross-channel analytics measures the performance of all your marketing channels and how they work together. 

  What are the benefits of cross-channel analytics 

  Cross-channel analytics offers a lot of marketing and business benefits. Here are the ones marketing managers love most.

  Get a complete view of the customer journey

  Implementing a cross-channel analytics solution is the only way to get a complete view of your customer journey. 

  Cross-channel marketing analytics lets you see your customer journey in high definition, allowing you to build comprehensive customer profiles using data from multiple sources across every touchpoint. 

  

  The result ? You get to understand how every customer behaves at every point of the customer journey, why they convert or leave your funnel, and which channels play the biggest role. 

  In short, you get to see why customers convert so you can learn how to convert more of them.

  Personalise the customer experience

  According to a McKinsey study, customers demand personalisation, and brands that excel at it generate 40% more revenue. Deliver the personalisation they desire and reap the benefits with cross-channel analytics. 

  When you understand the customer journey in detail, it becomes much easier to personalise your website and marketing efforts to their preferences and behaviours.

  Identify your most effective marketing channels

  Cross-channel marketing helps you understand your marketing efforts to see how every channel impacts conversions. 

  Take a look at the screenshot from Matomo below. Cross-channel analytics lets you get incredibly granular — we can see the number of conversions of organic search drives and the performance of individual search engines. 

  

  This makes it easy to identify your most effective marketing channels and allocate your resources appropriately. It also allows you to ask (and answer) which channels are the most effective.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Attribute conversions accurately 

  An attribution model decides how you assign credit for each customer conversion to different touchpoints on the customer journey. Without a cross-channel analytics solution, you’re stuck using a standard attribution model like first or last click. 

  These models will show you how customers first found your brand or which channel finally convinced them to convert, but it doesn’t help you understand the role all your channels played in the conversion. 

  Cross-channel analytics solves this attribution problem. Rather than attributing a conversion to the touchpoint that directly led to the sale, cross-channel data gives you the real picture and allows you to use multi-touch attribution to understand which touchpoints generate the most revenue.

  How to set up cross-channel analytics

  Now that you know what cross-channel analytics is and why you should use it, here’s how to set up your solution. 

  1. Determine your objectives

  Defining your marketing goals will help you build a more relevant and actionable cross-channel analytics solution. 

  If you want to improve marketing attribution, for example, you can choose a platform with that feature built-in. If you care about personalisation, you could choose a platform with A/B testing capabilities to measure the impact of your personalisation efforts. 

  1. Set relevant KPIs

  You’ll want to track relevant KPIs to measure the marketing effectiveness of each channel. Put top-of-the-funnel metrics aside and focus on conversion metrics. 

  These include :

  • Conversion rate
  • Average visit duration
  • Bounce rate

  1. Implement tracking and analytics tools

  Gathering customer data from every channel and centralising it in a single location is one of the biggest challenges of cross-channel analytics. Still, it’s made easier with the right tracking tool or analytics platform. 

  The trick is to choose a platform that lets you measure as many of your channels as possible in a single platform. With Matomo, for example, you can track search, paid search, social and email campaigns and your website analytics.

  1. Set up a multi-touch attribution model

  Now that you have all of your data in one place, you can set up a multi-touch attribution model that lets you understand the extent to which each marketing channel contributes to your overall success. 

  There are several attribution models to choose from, including :

  

  Each model has benefits and drawbacks, so choosing the right model for your organisation can be tricky. Rather than take a wild guess, evaluate each model against your marketing objectives, sales length cycle and data availability.

  For example, if you want to focus on optimising customer acquisition costs, a model that prioritises earlier touchpoints will be better. If you care about conversions, you might try a time decay model. 

  1. Turn data into insights with reports

  One of the big benefits of choosing a tool like Matomo, which consolidates data in one place, is that it significantly speeds up and simplifies reporting.

  When all the data is stored in one platform, you don’t need to spend hours combing through your social media platforms and copying and pasting analytics data into a spreadsheet. It’s all there and ready for you to run reports.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  1. Take action

  There’s no point implementing a cross-channel analytics system if you aren’t going to take action. 

  But where should you start ?

  Optimising your budgets and prioritising marketing spend is a great starting point. Use your cross-channel insights to find your most effective marketing channels (they’re the ones that convert the most customers or have the highest ROI) and allocate more of your budget to them. 

  You can also optimise the channels that aren’t pulling their weight if social media is letting you down ; for example, experiment with tactics like social commerce that could drive more conversions. Alternatively, you could choose to stop investing entirely in these channels.

  Cross-channel analytics best practices

  If you already have a cross-channel analytics solution, take things to the next level with the following best practices. 

  Use a centralised solution to track everything

  Centralising your data in one analytics tool can streamline your marketing efforts and help you stay on top of your data. It won’t just save you from tabbing between different browsers or copying and pasting everything into a spreadsheet, but it can also make it easier to create reports. 

  Think about consumer privacy 

  If you are looking at a new cross-channel analytics tool, consider how it accounts for data privacy regulations in your area. 

  You’re going to be collecting a lot of data, so it’s important to respect their privacy wishes. 

  It’s best to choose a platform like Matomo that complies with the strictest privacy laws (CCPA, GDPR, etc.).

  Monitor data in real time

  So, you’ve got a holistic view of your marketing efforts by integrating all your channels into a single tool ?

  Great, now go further by monitoring the impact of your marketing efforts in real time.

  

  With a web analytics platform like Matomo, you can see who visits your site, what they do, and where they come from through features like the visits log report, which even lets you view individual user sessions. This lets you measure the impact of posting on a particular social channel or launching a new offer. 

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Reallocate marketing budgets based on performance

  When you track every channel, you can use a multi-touch attribution model like position-based or time-decay to give every channel the credit it deserves. But don’t just credit each channel ; turn your valuable insights into action. 

  Use cross-channel attribution analytics data to reallocate your marketing budget to the most profitable channels or spend time optimising the channels that aren’t pulling their weight. 

  Cross-channel analytics platforms to get started with 

  The marketing analytics market is huge. Mordor Intelligence valued it at $6.31 billion in 2024 and expects it to reach $11.54 billion by 2029. Many of these platforms offer cross-channel analytics, but few can track the impact of multiple marketing channels in one place. 

  So, rather than force you to trawl through confusing product pages, we’ve shortlisted three of the best cross-channel analytics solutions. 

  Matomo

  

  Matomo is a web analytics platform that lets you collect and centralise your marketing data while giving you 100% accurate data. That includes search, social, e-commerce, campaign tracking data and comprehensive website analytics.

  Better still, you get the necessary tools to turn those insights into action. Custom reporting lets you track and visualise the metrics that matter, while conversion optimisation tools like built-in A/B testing, heatmaps, session recordings and more let you test your theories. 

  Google Analytics

  

  Google Analytics is the most popular and widely used tool on the market. The level of analysis and customisation you can do with it is impressive for a free tool. That includes tracking just about any event and creating reports from scratch. 

  Google Analytics provides some cross-channel marketing features and lets you track the impact of various channels, such as social and search, but there are a couple of drawbacks. 

  Privacy can be a concern because Google Analytics collects data from your customers for its own remarketing purposes. 

  It also uses data sampling to generate wider insights from a small subset of your data. This lack of accurate data reporting can cause you to generate false insights.

  With Google Analytics, you’ll also need to subscribe to additional tools to gain advanced insights into the user experience. So, consider that while this tool is free, you’ll need to pay for heatmaps, session recording and A/B testing tools to optimise effectively.

  Improvado

  

  Improvado is an analytics tool for sales and marketing teams that extracts thousands of metrics from hundreds of sources. It centralises data in data warehouses, from which you can create a range of marketing dashboards.

  While Improvado does have analytics capabilities, it is primarily an ETL (extraction, transform, load) tool for organisations that want to centralise all their data. That means marketers who aren’t familiar with data transformations may struggle to get their heads around the complexity of the platform.

  Make the most of cross-channel analytics with Matomo

  Cross-channel analytics is the only way to get a comprehensive view of your customer journey and understand how your channels work together to drive conversions.

  Then you’re dealing with so many channels and data ; keeping things as simple as possible is the key to success. That’s why over 1 million websites choose Matomo. 

  Our all-in-one analytics solution measures traditional web analytics, behavioural analytics, attribution and SEO, so you have 100% accurate data in one place. 

  Try it free for 21 days. No credit card required.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I accept the terms and conditions and data processing agreement (DPA)

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  
  

  Start improving your websites now

  

• FFmpeg RTSP drop rate increases when frame rate is reduced

  13 avril 2024, par Avishka Perera

  I need to read an RTSP stream, process the images individually in Python, and then write the images back to an RTSP stream. As the RTSP server, I am using Mediamtx [1]. For streaming, I am using FFmpeg [2].

  


  I have the following code that works perfectly fine. For simplification purposes, I am streaming three generated images.

  


  import time
import numpy as np
import subprocess

width, height = 640, 480
fps = 25
rtsp_server_address = f"rtsp://localhost:8554/mystream"

ffmpeg_cmd = [
    "ffmpeg",
    "-re",
    "-f",
    "rawvideo",
    "-pix_fmt",
    "rgb24",
    "-s",
    f"{width}x{height}",
    "-i",
    "-",
    "-r",
    str(fps),
    "-avoid_negative_ts",
    "make_zero",
    "-vcodec",
    "libx264",
    "-threads",
    "4",
    "-f",
    "rtsp",
    rtsp_server_address,
]
colors = np.array(
    [
        [255, 0, 0],
        [0, 255, 0],
        [0, 0, 255],
    ]
).reshape(3, 1, 1, 3)
images = (np.ones((3, width, height, 3)) * colors).astype(np.uint8)

if __name__ == "__main__":

    process = subprocess.Popen(ffmpeg_cmd, stdin=subprocess.PIPE)
    start = time.time()
    exported = 0
    while True:
        exported += 1
        next_time = start + exported / fps
        now = time.time()
        if next_time > now:
            sleep_dur = next_time - now
            time.sleep(sleep_dur)

        image = images[exported % 3]
        image_bytes = image.tobytes()

        process.stdin.write(image_bytes)
        process.stdin.flush()

    process.stdin.close()
    process.wait()


  


  The issue is, that I need to run this at 10 fps because the processing step is heavy and can only afford 10 fps. Hence, as I reduce the frame rate from 25 to 10, the drop rate increases from 0% to 100%. And after a few iterations, I get a BrokenPipeError: [Errno 32] Broken pipe. Refer to the appendix for the complete log.

  


  As an alternative, I can use OpenCV compiled from source with GStreamer [3], but I prefer using FFmpeg to make the shipping process simple. Since compiling OpenCV from source can be tedious and dependent on the system.

  


  References

  


  [1] Mediamtx (formerly rtsp-simple-server) : https://github.com/bluenviron/mediamtx

  


  [2] FFmpeg : https://github.com/FFmpeg/FFmpeg

  


  [3] Compile OpenCV with GStreamer : https://github.com/bluenviron/mediamtx?tab=readme-ov-file#opencv

  


  Appendix

  


  Creating the source stream

  


  To instantiate the unprocessed stream, I use the following command. This streams the content of my webcam as and RTSP stream.

  


  ffmpeg -video_size 1280x720 -i /dev/video0  -avoid_negative_ts make_zero -vcodec libx264 -r 10 -f rtsp rtsp://localhost:8554/webcam


  


  Error log

  


  ffmpeg version 6.1.1 Copyright (c) 2000-2023 the FFmpeg developers&#xA;  built with gcc 12.3.0 (conda-forge gcc 12.3.0-5)&#xA;  configuration: --prefix=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_plac --cc=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/x86_64-conda-linux-gnu-cc --cxx=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/x86_64-conda-linux-gnu-c&#x2B;&#x2B; --nm=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/x86_64-conda-linux-gnu-nm --ar=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/x86_64-conda-linux-gnu-ar --disable-doc --disable-openssl --enable-demuxer=dash --enable-hardcoded-tables --enable-libfreetype --enable-libharfbuzz --enable-libfontconfig --enable-libopenh264 --enable-libdav1d --enable-gnutls --enable-libmp3lame --enable-libvpx --enable-libass --enable-pthreads --enable-vaapi --enable-libopenvino --enable-gpl --enable-libx264 --enable-libx265 --enable-libaom --enable-libsvtav1 --enable-libxml2 --enable-pic --enable-shared --disable-static --enable-version3 --enable-zlib --enable-libopus --pkg-config=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/pkg-config&#xA;  libavutil      58. 29.100 / 58. 29.100&#xA;  libavcodec     60. 31.102 / 60. 31.102&#xA;  libavformat    60. 16.100 / 60. 16.100&#xA;  libavdevice    60.  3.100 / 60.  3.100&#xA;  libavfilter     9. 12.100 /  9. 12.100&#xA;  libswscale      7.  5.100 /  7.  5.100&#xA;  libswresample   4. 12.100 /  4. 12.100&#xA;  libpostproc    57.  3.100 / 57.  3.100&#xA;Input #0, rawvideo, from &#x27;fd:&#x27;:&#xA;  Duration: N/A, start: 0.000000, bitrate: 184320 kb/s&#xA;  Stream #0:0: Video: rawvideo (RGB[24] / 0x18424752), rgb24, 640x480, 184320 kb/s, 25 tbr, 25 tbn&#xA;Stream mapping:&#xA;  Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))&#xA;[libx264 @ 0x5e2ef8b01340] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2&#xA;[libx264 @ 0x5e2ef8b01340] profile High 4:4:4 Predictive, level 2.2, 4:4:4, 8-bit&#xA;[libx264 @ 0x5e2ef8b01340] 264 - core 164 r3095 baee400 - H.264/MPEG-4 AVC codec - Copyleft 2003-2022 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=4 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=10 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00&#xA;Output #0, rtsp, to &#x27;rtsp://localhost:8554/mystream&#x27;:&#xA;  Metadata:&#xA;    encoder         : Lavf60.16.100&#xA;  Stream #0:0: Video: h264, yuv444p(tv, progressive), 640x480, q=2-31, 10 fps, 90k tbn&#xA;    Metadata:&#xA;      encoder         : Lavc60.31.102 libx264&#xA;    Side data:&#xA;      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A&#xA;[vost#0:0/libx264 @ 0x5e2ef8b01080] Error submitting a packet to the muxer: Broken pipe   &#xA;[out#0/rtsp @ 0x5e2ef8afd780] Error muxing a packet&#xA;[out#0/rtsp @ 0x5e2ef8afd780] video:1kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown&#xA;frame=    1 fps=0.1 q=-1.0 Lsize=N/A time=00:00:04.70 bitrate=N/A dup=0 drop=70 speed=0.389x    &#xA;[libx264 @ 0x5e2ef8b01340] frame I:16    Avg QP: 6.00  size:   147&#xA;[libx264 @ 0x5e2ef8b01340] frame P:17    Avg QP: 9.94  size:   101&#xA;[libx264 @ 0x5e2ef8b01340] frame B:17    Avg QP: 9.94  size:    64&#xA;[libx264 @ 0x5e2ef8b01340] consecutive B-frames: 50.0%  0.0% 42.0%  8.0%&#xA;[libx264 @ 0x5e2ef8b01340] mb I  I16..4: 81.3% 18.7%  0.0%&#xA;[libx264 @ 0x5e2ef8b01340] mb P  I16..4: 52.9%  0.0%  0.0%  P16..4:  0.0%  0.0%  0.0%  0.0%  0.0%    skip:47.1%&#xA;[libx264 @ 0x5e2ef8b01340] mb B  I16..4:  0.0%  5.9%  0.0%  B16..8:  0.1%  0.0%  0.0%  direct: 0.0%  skip:94.0%  L0:56.2% L1:43.8% BI: 0.0%&#xA;[libx264 @ 0x5e2ef8b01340] 8x8 transform intra:15.4% inter:100.0%&#xA;[libx264 @ 0x5e2ef8b01340] coded y,u,v intra: 0.0% 0.0% 0.0% inter: 0.0% 0.0% 0.0%&#xA;[libx264 @ 0x5e2ef8b01340] i16 v,h,dc,p: 97%  0%  3%  0%&#xA;[libx264 @ 0x5e2ef8b01340] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu:  0%  0% 100%  0%  0%  0%  0%  0%  0%&#xA;[libx264 @ 0x5e2ef8b01340] Weighted P-Frames: Y:52.9% UV:52.9%&#xA;[libx264 @ 0x5e2ef8b01340] ref P L0: 88.9%  0.0%  0.0% 11.1%&#xA;[libx264 @ 0x5e2ef8b01340] kb/s:8.27&#xA;Conversion failed!&#xA;Traceback (most recent call last):&#xA;  File "/home/avishka/projects/read-process-stream/minimal-ffmpeg-error.py", line 58, in <module>&#xA;    process.stdin.write(image_bytes)&#xA;BrokenPipeError: [Errno 32] Broken pipe&#xA;</module>