Recherche avancée
Autres articles (112)
-
Ecrire une actualité
21 juin 2013, parPrésentez les changements dans votre MédiaSPIP ou les actualités de vos projets sur votre MédiaSPIP grâce à la rubrique actualités.
Dans le thème par défaut spipeo de MédiaSPIP, les actualités sont affichées en bas de la page principale sous les éditoriaux.
Vous pouvez personnaliser le formulaire de création d’une actualité.
Formulaire de création d’une actualité Dans le cas d’un document de type actualité, les champs proposés par défaut sont : Date de publication ( personnaliser la date de publication ) (...) -
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 (...) -
Script d’installation automatique de MediaSPIP
25 avril 2011, parAfin de palier aux difficultés d’installation dues principalement aux dépendances logicielles coté serveur, un script d’installation "tout en un" en bash a été créé afin de faciliter cette étape sur un serveur doté d’une distribution Linux compatible.
Vous devez bénéficier d’un accès SSH à votre serveur et d’un compte "root" afin de l’utiliser, ce qui permettra d’installer les dépendances. Contactez votre hébergeur si vous ne disposez pas de cela.
La documentation de l’utilisation du script d’installation (...)
Sur d’autres sites (9863)
-
RoQ on Dreamcast
18 mars 2011, par Multimedia Mike — Sega DreamcastI have been working on that challenge to play back video on the Sega Dreamcast. To review, I asserted that the RoQ format would be a good fit for the Sega Dreamcast hardware. The goal was to play 640x480 video at 30 frames/second. Short version : I have determined that it is possible to decode such video in real time. However, I ran into certain data rate caveats.
First off : Have you ever wondered if the Dreamcast can read an 80mm optical disc ? It can ! I discovered this when I only had 60 MB of RoQ samples to burn on a disc and a spindle full of these 210MB-capacity 80mm CD-Rs that I never have occasion to use.
New RoQ Library
There are open source RoQ decoders out there but I decided to write a new one. A few reasons : 1) RoQ is so simple that I didn’t think it would take too long ; 2) it would be nice to have a RoQ library that is license-compatible (BSD-like) with the rest of the KallistiOS distribution ; 3) the idroq.tar.gz distribution, while license-compatible, has enough issues that I didn’t want to correct it.Thankfully, I was correct about the task not being too difficult : I put together a new RoQ decoder in short order. I’m a bit embarrassed to admit that the part I had the most trouble with was properly converting YUV -> RGB.
About the approach I took : While the original idroq.tar.gz decoder maintains YUV 4:2:0 codebooks (which led to chroma bugs during motion compensation) and FFmpeg’s decoder maintains YUV 4:4:4 codebooks, this decoder is built to convert the YUV 4:2:0 vectors into RGB565 vectors during the vector unpacking phase. Thus, the entire frame is rendered in RGB565 — no lengthy YUV -> RGB conversion after decoding — and all pixels are shuffled around as 16-bit units (minor speedup vs. shuffling everything as bytes).I also entertained the idea of maintaining YUYV codebooks (since the DC supports that colorspace as a texture format). But I scrapped that idea when I remembered it would lead to the same chroma bleeding problem seen in the original idroq.tar.gz decoder.
Onto The Dreamcast
I developed the library on a Linux computer, allowing it to output a series of PNM files for visual verification and debugging. Dropping it into a basic DC/KOS-compatible program was trivial and the first order of business was profiling.At first, I profiled the entire decode operation : open file, then read and decode each chunk while tossing away the results. I was roundly disappointed to see that, e.g., an 8.5-second RoQ sample needed a little more than 20 seconds to complete. Not real time. I performed a series of optimizations on the decoding library that netted notable performance gains when profiling on Linux. When I brought these same optimizations over to the DC, decoding time didn’t improve at all. This was my first suspicion that perhaps my assumptions regarding the DC’s optical drive’s data rate were not correct.
Dreamcast Data Rate Profiling
Let’s start with some definitions : In terms of data rate, an ’X’, i.e., 1X is the minimum data rate needed to read CD quality audio from a disc. At that speed, a drive should be able to stream 75 sectors each second. When reading mode 1/form 1 CD-ROM data, each sector has 2048 bytes (2 kbytes), so a single-speed data rate should achieve 150 kbytes/sec.The Dreamcast is supposed to possess a 12X optical drive. This would imply a maximum data rate of 150 kbytes/sec * 12 = 1800 kbytes/sec.
Rigging up a trivial experiment using the RoQ samples burned on a few different CD-R discs, the best data rate I can see is about 500-525 kbytes/sec, or around 3.5X.
Where’s the discrepancy ? My first theory has to do with the fact that not all optical media is created equal. This is why optical drives often advertise a slew of numbers which refer to the best theoretical speed for reading a CD vs. writing a CD-R vs. writing a CD-RW, etc. Perhaps the DC drive can’t read CD-Rs very quickly. To test this theory, I tried streaming a large file from a conventionally mastered CD-ROM. This worked well for the closest CD-ROM I had on hand : I was able to stream data at a rate that works out to about 6.5X.
I smell a science project for another evening : Profiling read speeds from a mastered CD-ROM, burned CD-R, and also a mastered GD-ROM, on each of the 3 Dreamcast consoles I possess (I’ve heard that there’s variance between optical drives depending on manufacturing run).
The Good News
I added a little finer-grained code to profile just the video decoding functions. The good news is that the decoder meets my real time goals : That 8.5-second RoQ sample encoded at 640x480x30fps makes its way through the video decoding functions on the DC in a little less than 5 seconds. If the optical drive can supply the data fast enough, the video decoder can take care of the rest.The RoQ encoder included with FFmpeg does not honor any bitrate parameters. Instead, I encoded the same file at 320x240. It reportedly decoded in real time and can be streamed in real time as well.
I say "reportedly" because I’m simply working from textual output at this point ; the next phase is to hook the decoder up to the display hardware.
-
ffmpeg GRAY16 stream over network
28 novembre 2023, par Norbert P.Im working in a school project where we need to use depth cameras. The camera produces color and depth (in other words 16bit grayscale image). We decided to use ffmpeg, as later on compression could be very useful. For now we got some basic stream running form one PC to other. These settings include :


- 

- rtmp
- flv as container
- pixel format AV_PIX_FMT_YUV420P
- codec AV_CODEC_ID_H264










The problem we are having is with grayscale image. Not every codec is able to cope with this format, so as not every protocol able to work with given codec. I got some settings "working" but receiver side is just stuck on avformat_open_input() method.
I have also tested it with commandline where ffmpeg is listening for connection and same happens.


I include a minimum "working" example of client code. Server can be tested with "ffmpeg.exe -f apng -listen 1 -i rtmp ://localhost:9999/stream/stream1 -c copy -f apng -listen 1 rtmp ://localhost:2222/live/l" or code below. I get no warnings, ffmpeg is newest version installed with "vcpkg install —triplet x64-windows ffmpeg[ffmpeg,ffprobe,zlib]" on windows or packet manager on linux.


The question : Did I miss something ? How do I get it to work ? If you have any better ideas I would very gladly consider them. In the end I need 16 bits of lossless transmission, could be split between channels etc. which I also tried with same effect.


Client code that would have camera and connect to server :


extern "C" {
#include <libavutil></libavutil>opt.h>
#include <libavcodec></libavcodec>avcodec.h>
#include <libavutil></libavutil>channel_layout.h>
#include <libavutil></libavutil>common.h>
#include <libavformat></libavformat>avformat.h>
#include <libavcodec></libavcodec>avcodec.h>
#include <libavutil></libavutil>imgutils.h>
}

int main() {

 std::string container = "apng";
 AVCodecID codec_id = AV_CODEC_ID_APNG;
 AVPixelFormat pixFormat = AV_PIX_FMT_GRAY16BE;

 AVFormatContext* format_ctx;
 AVCodec* out_codec;
 AVStream* out_stream;
 AVCodecContext* out_codec_ctx;
 AVFrame* frame;
 uint8_t* data;

 std::string server = "rtmp://localhost:9999/stream/stream1";

 int width = 1280, height = 720, fps = 30, bitrate = 1000000;

 //initialize format context for output with flv and no filename
 avformat_alloc_output_context2(&format_ctx, nullptr, container.c_str(), server.c_str());
 if (!format_ctx) {
 return 1;
 }

 //AVIOContext for accessing the resource indicated by url
 if (!(format_ctx->oformat->flags & AVFMT_NOFILE)) {
 int avopen_ret = avio_open(&format_ctx->pb, server.c_str(),
 AVIO_FLAG_WRITE);// , nullptr, nullptr);
 if (avopen_ret < 0) {
 fprintf(stderr, "failed to open stream output context, stream will not work\n");
 return 1;
 }
 }


 const AVCodec* tmp_out_codec = avcodec_find_encoder(codec_id);
 //const AVCodec* tmp_out_codec = avcodec_find_encoder_by_name("hevc");
 out_codec = const_cast(tmp_out_codec);
 if (!(out_codec)) {
 fprintf(stderr, "Could not find encoder for '%s'\n",
 avcodec_get_name(codec_id));

 return 1;
 }

 out_stream = avformat_new_stream(format_ctx, out_codec);
 if (!out_stream) {
 fprintf(stderr, "Could not allocate stream\n");
 return 1;
 }

 out_codec_ctx = avcodec_alloc_context3(out_codec);

 const AVRational timebase = { 60000, fps };
 const AVRational dst_fps = { fps, 1 };
 av_log_set_level(AV_LOG_VERBOSE);
 //codec_ctx->codec_tag = 0;
 //codec_ctx->codec_id = codec_id;
 out_codec_ctx->codec_type = AVMEDIA_TYPE_VIDEO;
 out_codec_ctx->width = width;
 out_codec_ctx->height = height;
 out_codec_ctx->gop_size = 1;
 out_codec_ctx->time_base = timebase;
 out_codec_ctx->pix_fmt = pixFormat;
 out_codec_ctx->framerate = dst_fps;
 out_codec_ctx->time_base = av_inv_q(dst_fps);
 out_codec_ctx->bit_rate = bitrate;
 //if (fctx->oformat->flags & AVFMT_GLOBALHEADER)
 //{
 // codec_ctx->flags |= AV_CODEC_FLAG_GLOBAL_HEADER;
 //}

 out_stream->time_base = out_codec_ctx->time_base; //will be set afterwards by avformat_write_header to 1/1000

 int ret = avcodec_parameters_from_context(out_stream->codecpar, out_codec_ctx);
 if (ret < 0)
 {
 fprintf(stderr, "Could not initialize stream codec parameters!\n");
 return 1;
 }

 AVDictionary* codec_options = nullptr;
 av_dict_set(&codec_options, "tune", "zerolatency", 0);

 // open video encoder
 ret = avcodec_open2(out_codec_ctx, out_codec, &codec_options);
 if (ret < 0)
 {
 fprintf(stderr, "Could not open video encoder!\n");
 return 1;
 }
 av_dict_free(&codec_options);

 out_stream->codecpar->extradata_size = out_codec_ctx->extradata_size;
 out_stream->codecpar->extradata = static_cast(av_mallocz(out_codec_ctx->extradata_size));
 memcpy(out_stream->codecpar->extradata, out_codec_ctx->extradata, out_codec_ctx->extradata_size);

 av_dump_format(format_ctx, 0, server.c_str(), 1);

 frame = av_frame_alloc();

 int sz = av_image_get_buffer_size(pixFormat, width, height, 32);
#ifdef _WIN32
 data = (uint8_t*)_aligned_malloc(sz, 32);
 if (data == NULL)
 return ENOMEM;
#else
 ret = posix_memalign(reinterpret_cast(&data), 32, sz);
#endif
 av_image_fill_arrays(frame->data, frame->linesize, data, pixFormat, width, height, 32);
 frame->format = pixFormat;
 frame->width = width;
 frame->height = height;
 frame->pts = 1;
 if (avformat_write_header(format_ctx, nullptr) < 0) //Header making problems!!!
 {
 fprintf(stderr, "Could not write header!\n");
 return 1;
 }

 printf("stream time base = %d / %d \n", out_stream->time_base.num, out_stream->time_base.den);

 double inv_stream_timebase = (double)out_stream->time_base.den / (double)out_stream->time_base.num;
 printf("Init OK\n");
 /* Init phase end*/
 int dts = 0;
 int frameNo = 0;

 while (true) {
 //Fill dummy frame with something
 for (int y = 0; y < height; y++) {
 uint16_t color = ((y + frameNo) * 256) % (256 * 256);
 for (int x = 0; x < width; x++) {
 data[x+y*width] = color;
 }
 }

 memcpy(frame->data[0], data, 1280 * 720 * sizeof(uint16_t));
 AVPacket* pkt = av_packet_alloc();

 int ret = avcodec_send_frame(out_codec_ctx, frame);
 if (ret < 0)
 {
 fprintf(stderr, "Error sending frame to codec context!\n");
 return ret;
 }
 while (ret >= 0) {
 ret = avcodec_receive_packet(out_codec_ctx, pkt);
 if (ret == AVERROR(EAGAIN) || ret == AVERROR_EOF)
 break;
 else if (ret < 0) {
 fprintf(stderr, "Error during encoding\n");
 break;
 }
 pkt->dts = dts;
 pkt->pts = dts;
 dts += 33;
 av_write_frame(format_ctx, pkt);
 frameNo++;
 av_packet_unref(pkt);
 }
 printf("Streamed %d frames\n", frameNo);
 }
 return 0;
}


And part of server that should receive. code where is stops and waits


extern "C" {
#include <libavcodec></libavcodec>avcodec.h>
#include <libavformat></libavformat>avformat.h>
#include <libavformat></libavformat>avio.h>
}

int main() {
 AVFormatContext* fmt_ctx = NULL;
 av_log_set_level(AV_LOG_VERBOSE);
 AVDictionary* options = nullptr;
 av_dict_set(&options, "protocol_whitelist", "file,udp,rtp,tcp,rtmp,rtsp,hls", 0);
 av_dict_set(&options, "timeout", "500000", 0); // Timeout in microseconds 

//Next Line hangs 
 int ret = avformat_open_input(&fmt_ctx, "rtmp://localhost:9999/stream/stream1", NULL, &options);
 if (ret != 0) {
 fprintf(stderr, "Could not open RTMP stream\n");
 return -1;
 }

 // Find the first video stream
 ret = avformat_find_stream_info(fmt_ctx, nullptr);
 if (ret < 0) {
 return ret;
 }
 //...
} 



Edit :
I tried to just create a animated png and tried to stream that from the console to another console window to avoid any programming mistakes on my side. It was the same, I just could not get 16 PNG encoded stream to work. I hung trying to receive and closed when the file ended with in total zero frames received.


I managed to get other thing working :
To not encode gray frames with YUV420, I installed ffmpeg with libx264 support (was thinking is the same as H264, which in code is, but it adds support to new pixel formats). Used H264 again but with GRAY8 with doubled image width and reconstructing the image on the other side.


Maybe as a side note, I could not get any other formats to work. Is "flv" the only option here ? Could I get more performance if I changed it to... what ?


-
What is Audience Segmentation ? The 5 Main Types & Examples
16 novembre 2023, par Erin — Analytics TipsThe days of mass marketing with the same message for millions are long gone. Today, savvy marketers instead focus on delivering the most relevant message to the right person at the right time.
They do this at scale by segmenting their audiences based on various data points. This isn’t an easy process because there are many types of audience segmentation. If you take the wrong approach, you risk delivering irrelevant messages to your audience — or breaking their trust with poor data management.
In this article, we’ll break down the most common types of audience segmentation, share examples highlighting their usefulness and cover how you can segment campaigns without breaking data regulations.
What is audience segmentation ?
Audience segmentation is when you divide your audience into multiple smaller specific audiences based on various factors. The goal is to deliver a more targeted marketing message or to glean unique insights from analytics.
It can be as broad as dividing a marketing campaign by location or as specific as separating audiences by their interests, hobbies and behaviour.
Audience segmentation inherently makes a lot of sense. Consider this : an urban office worker and a rural farmer have vastly different needs. By targeting your marketing efforts towards agriculture workers in rural areas, you’re honing in on a group more likely to be interested in farm equipment.
Audience segmentation has existed since the beginning of marketing. Advertisers used to select magazines and placements based on who typically read them. They would run a golf club ad in a golf magazine, not in the national newspaper.
How narrow you can make your audience segments by leveraging multiple data points has changed.
Why audience segmentation matters
In a survey by McKinsey, 71% of consumers said they expected personalisation, and 76% get frustrated when a vendor doesn’t deliver.
These numbers reflect expectations from consumers who have actively engaged with a brand — created an account, signed up for an email list or purchased a product.
They expect you to take that data and give them relevant product recommendations — like a shoe polishing kit if you bought nice leather loafers.
If you don’t do any sort of audience segmentation, you’re likely to frustrate your customers with post-sale campaigns. If, for example, you just send the same follow-up email to all customers, you’d damage many relationships. Some might ask : “What ? Why would you think I need that ?” Then they’d promptly opt out of your email marketing campaigns.
To avoid that, you need to segment your audience so you can deliver relevant content at all stages of the customer journey.
5 key types of audience segmentation
To help you deliver the right content to the right person or identify crucial insights in analytics, you can use five types of audience segmentation : demographic, behavioural, psychographic, technographic and transactional.
Demographic segmentation
Demographic segmentation is when you segment a larger audience based on demographic data points like location, age or other factors.
The most basic demographic segmentation factor is location, which is easy to leverage in marketing efforts. For example, geographic segmentation can use IP addresses and separate marketing efforts by country.
But more advanced demographic data points are becoming increasingly sensitive to handle. Especially in Europe, GDPR makes advanced demographics a more tentative subject. Using age, education level and employment to target marketing campaigns is possible. But you need to navigate this terrain thoughtfully and responsibly, ensuring meticulous adherence to privacy regulations.
Potential data points :
- Location
- Age
- Marital status
- Income
- Employment
- Education
Example of effective demographic segmentation :
A clothing brand targeting diverse locations needs to account for the varying weather conditions. In colder regions, showcasing winter collections or insulated clothing might resonate more with the audience. Conversely, in warmer climates, promoting lightweight or summer attire could be more effective.
Here are two ads run by North Face on Facebook and Instagram to different audiences to highlight different collections :
Each collection is featured differently and uses a different approach with its copy and even the media. With social media ads, targeting people based on advanced demographics is simple enough — you can just single out the factors when making your campaign. But if you don’t want to rely on these data-mining companies, that doesn’t mean you have no options for segmentation.
Consider allowing people to self-select their interests or preferences by incorporating a short survey within your email sign-up form. This simple addition can enhance engagement, decrease bounce rates, and ultimately improve conversion rates, offering valuable insights into audience preferences.
This is a great way to segment ethically and without the need of data-mining companies.
Behavioural segmentation
Behavioural segmentation segments audiences based on their interaction with your website or app.
You use various data points to segment your target audience based on their actions.
Potential data points :
- Page visits
- Referral source
- Clicks
- Downloads
- Video plays
- Goal completion (e.g., signing up for a newsletter or purchasing a product)
Example of using behavioural segmentation to improve campaign efficiency :
One effective method involves using a web analytics tool such as Matomo to uncover patterns. By segmenting actions like specific clicks and downloads, pinpoint valuable trends—identifying actions that significantly enhance visitor conversions.
For instance, if a case study video substantially boosts conversion rates, elevate its prominence to capitalise on this success.
Then, you can set up a conditional CTA within the video player. Make it pop up after the user has watched the entire video. Use a specific form and sign them up to a specific segment for each case study. This way, you know the prospect’s ideal use case without surveying them.
This is an example of behavioural segmentation that doesn’t rely on third-party cookies.
Psychographic segmentation
Psychographic segmentation is when you segment audiences based on your interpretation of their personality or preferences.
Potential data points :
- Social media patterns
- Follows
- Hobbies
- Interests
Example of effective psychographic segmentation :
Here, Adidas segments its audience based on whether they like cycling or rugby. It makes no sense to show a rugby ad to someone who’s into cycling and vice versa. But to rugby athletes, the ad is very relevant.
If you want to avoid social platforms, you can use surveys about hobbies and interests to segment your target audience in an ethical way.
Technographic segmentation
Technographic segmentation is when you single out specific parts of your audience based on which hardware or software they use.
Potential data points :
- Type of device used
- Device model or brand
- Browser used
Example of segmenting by device type to improve user experience :
Upon noticing a considerable influx of tablet users accessing their platform, a leading news outlet decided to optimise their tablet browsing experience. They overhauled the website interface, focusing on smoother navigation and better readability for tablet users. These changes offered tablet users a seamless and enjoyable reading experience tailored precisely to their device.
Transactional segmentation
Transactional segmentation is when you use your customers’ purchase history to better target your marketing message to their needs.
When consumers prefer personalisation, they typically mean based on their actual transactions, not their social media profiles.
Potential data points :
- Average order value
- Product categories purchased within X months
- X days since the last purchase of a consumable product
Example of effective transactional segmentation :
A pet supply store identifies a segment of customers consistently purchasing cat food but not other pet products. They create targeted email campaigns offering discounts or loyalty rewards specifically for cat-related items to encourage repeat purchases within this segment.
If you want to improve customer loyalty and increase revenue, the last thing you should do is send generic marketing emails. Relevant product recommendations or coupons are the best way to use transactional segmentation.
B2B-specific : Firmographic segmentation
Beyond the five main segmentation types, B2B marketers often use “firmographic” factors when segmenting their campaigns. It’s a way to segment campaigns that go beyond the considerations of the individual.
Potential data points :
- Company size
- Number of employees
- Company industry
- Geographic location (office)
Example of effective firmographic segmentation :
Companies of different sizes won’t need the same solution — so segmenting leads by company size is one of the most common and effective examples of B2B audience segmentation.
The difference here is that B2B campaigns are often segmented through manual research. With an account-based marketing approach, you start by researching your potential customers. You then separate the target audience into smaller segments (or even a one-to-one campaign).
Start segmenting and analysing your audience more deeply with Matomo
Segmentation is a great place to start if you want to level up your marketing efforts. Modern consumers expect to get relevant content, and you must give it to them.
But doing so in a privacy-sensitive way is not always easy. You need the right approach to segment your customer base without alienating them or breaking regulations.
That’s where Matomo comes in. Matomo champions privacy compliance while offering comprehensive insights and segmentation capabilities. With robust privacy controls and cookieless configuration, it ensures GDPR and other regulations are met, empowering data-driven decisions without compromising user privacy.
Take advantage of our 21-day free trial to get insights that can help you improve your marketing strategy and better reach your target audience. No credit card required.
Syndiquer tout le site
