Recherche avancée

Sur d’autres sites (4930)

• avcodec : rename the AV1 profiles

  29 mars 2018, par James Almer

  avcodec : rename the AV1 profiles
  Use the proper names instead of numbers
  Signed-off-by : James Almer <jamrial@gmail.com>
  

  • [DBH] libavcodec/avcodec.h
  • [DBH] libavcodec/libaomenc.c

• 5 perfect feature combinations to use with Heatmaps and Session Recordings

  28 janvier 2020, par Jake Thornton — Uncategorized

  Gaining valuable insights by simply creating a heatmap or setting up recordings on your most important web pages is a good start, but using the Heatmaps and Session Recordings features in combination with other Matomo features is where the real magic happens.

  If you’re serious about significantly increasing conversions on your website to impact your bottom line, you need to accurately answer these questions :

  1. Are you analysing the most important pages that influence conversions ?
  2. Are you segmenting heatmaps and recordings towards your most valuable visitors ? 
  3. Are you using these features to create better customer journeys ?
  4. Have you identified current pain points for visitors before setting up heatmaps and recordings ?
  5. Are you learning what content resonates best for your audience ?

  With Matomo Analytics, you have the ability to integrate heatmaps and session recordings with all the features of a powerful web analytics platform, which means you get the complete picture of your visitor’s experience of your website.

  Here are five features that work with Heatmaps and Session Recordings to maximise conversions :

  1. Behaviour feature with Heatmaps

  Before creating heatmaps on pages you think are most important to your website, first check out Behaviour – Pages. Here you get valuable information around unique pageviews, bounce rate, average time on page and exit rates for every page on your website.

  Use this data as your starting point for heatmaps. Here you’ll identify current pain points for your visitors before using heatmaps to analyse their interactions on these pages.

  Here’s how to use the behaviour feature to determine which pages to setup heatmaps on :

  

  • Make sure you know what pages are generating the most unique page views, it could be your blog rather than your homepage
  • Which pages have the highest bounce rates – can you make some quick changes above-the-fold and see if this makes a difference
  • When the average time on page is high, why are visitors so engaged with these pages ? What keeps them reading ? Setup a heatmap to learn more
  • Reduce exit rates by moving them along to other pages on your website
  • Determine some milestones you want to achieve e.g. use heatmaps as your visual guide to improve average time on page, bounce rates and exit rates. A milestone could be that the exit rate for your previous blog was 34%, work towards getting this down to 30%

  Learn more about the Behaviour feature

  2. Ecommerce feature and Custom Segments

  If you run an ecommerce business, you may want to learn only about visitors who are more likely to be your customers. For example, if you find 65% of product sales come from customers based in New York, but visits to your product pages are from every state in the USA, how can you learn more specifically about visitors only from New York ?

  Using Segments to target a particular audience :

  • First, make sure you have created heatmaps and recordings on the popular product pages you want to learn about your visitor’s interactions
  • Note : Make sure the segment you create generates enough pageviews to apply a heatmap for more accurate results. We recommend a minimum of 1,000 page views per sample size.
  • Then create a custom Segment – search Ecommerce and find the Product Name and select the product. Learn how to do this here.

  

  Click on ‘Add a new segment’ or on the ‘edit’ link next to an existing segment name to open the segment editor :

  

  Click on any item on the left to see the list of information you can segment by. In this case search “City”, then select “Is” and in the third column search “New York” (example in the image above) :

  

  You can also use the search box at the bottom to search through the whole list.

  • This will give you insights across the Matomo platform based only on customers who purchased this product
  • Then go to the Ecommerce feature – and find Sales. Here you will learn what your most popular locations are for your product sales.

  

  • Once you know the location you want to segment, go back and update the custom Segment you just created. Click on the edit pencil icon and update it by selecting Add AND condition, and add the sub group you would like to track on the product page. In this example, select City – New York. Click Save & Apply.

  Now you should have successfully created a segment for your popular product page with visitors only from New York.

  Check out the heatmap or recordings you created for this page. You may be very surprised to see how this segment engaged with your website compared to all website visitors.

  Note : If you run a lead generation website you can use the Goals feature instead of Ecommerce to track the success metrics you need.

  Learn more about Segmentation

  3. Visitor Profiles within Session Recordings

  Seeing visitor location, device, OS and browser for your recordings is very valuable, but it’s even more valuable to integrate visitor profiles with session recordings as you get to see everything that visitor has done on your website … ever ! 

  What pages they visited before/after the recording, what actions they took, how long they spent on your website etc. All this is captured in the visitor profile of every individual session recording so you can see where exactly engaged viewers are in their journey with your business, for example :

  • How has this visitor behaved on your website in the past ? 
  • Is this visitor already a customer ? 
  • Is this the visitors first time to your website and
  • What other pages on your website are they interested in seeing in this session ?

  Use the visitor profiles feature within session recordings to understand the users better when watching each session.

  You get the full picture of what role the page you recorded played in the overall experience of your website’s visitor. And more importantly, to see if they took the desired action you wanted them to take.

  

  Learn more about Visitor Profiles

  4. Funnels feature (premium feature)

  The Funnels feature lets you see the customer journey from the first entry page through to the conversion page.

  

  Once you create a funnel, you can see the % of visitors who drop off between pages on their way to converting.

  In our example, you may then see page one to page two has a drop-off rate of 47%. Page two to page three 95% users drop-off rate and page three to page four 97.3% users drop-off rate.

  Why is the drop-off rate so high from page two to page three and why is the drop-off rate so low from page three to page four ?

  So, you may need to simplify things on page one because you may unknowingly be offering your visitor an easy way out of the funnel. Maybe the visitor is stuck reading your content and not understanding the value of your offering.

  Small tip for session recordings …

  With session recordings especially you can see firsthand through live recordings where exactly visitors click away from the page which exits them from your conversion funnel. Take note to see if this is a recurring issue with other visitors, then take action into fixing this hole.

  Whatever the case, work towards reducing drop-off rates through your conversion funnels by discovering where the problems exist, make changes and learn how these changes affect engagement through heatmaps and recordings.

  Learn more about Funnels

  5. A/B Testing feature (premium feature)

  Following on from the example with the Funnels feature, once you identify there is a problem in your conversion funnel, how do you know what is preventing visitors from taking an action that pushes them to the next page in the funnel ? You need to test different variations of content to see what works best for your visitors.

  A/B Testing lets you test a variety of things, including :

  • different headlines 
  • less copy vs more copy 
  • different call-to-actions
  • different colour schemes
  • entirely different page layouts

  

  Once you’ve created two or more variations of specific landing pages in the conversion funnel, see how visitors interacted differently between the variations of landing pages through your heatmaps and recordings.

  

  You may see that your visitors have scrolled further down the page because more content was provided or an important CTA button was clicked more due to a colour change. Whatever the case, using A/B testing with heatmaps and session recordings is an effective combination for increasing user engagement.

  Learn more about A/B Testing

  The conversion rate optimization (CRO) strategy

  CRO is the process of learning what the most valuable content/aspect of your website is and how to best optimize this for your visitors to increase conversion chances. 

  Heatmaps and session recordings play a vital role in this strategy, but it’s how you work these features in tandem with other valuable Matomo features that will give you the most actionable insights you need to grow your business.

  Want to learn how to create an effective CRO strategy ?

  Learn CRO strategies

• FFmpeg - MJPEG decoding - getting different values

  27 décembre 2016, par ahmadh

  I have a set of JPEG frames which I am muxing into an avi, which gives me a mjpeg video. This is the command I run on the console :

  ffmpeg -y -start_number 0 -i %06d.JPEG -codec copy vid.avi

  When I try to demux the video using ffmpeg C api, I get frames which are slightly different in values. Demuxing code looks something like this :

  AVFormatContext* fmt_ctx = NULL;
  
  AVCodecContext* cdc_ctx = NULL;
  
  AVCodec* vid_cdc = NULL;
  
  int ret;
  
  unsigned int height, width;
  
  
  
  ....
  
  // read_nframes is the number of frames to read
  
  output_arr = new unsigned char [height * width * 3 * 
  
                                  sizeof(unsigned char) * read_nframes];
  
  
  
  avcodec_open2(cdc_ctx, vid_cdc, NULL);
  
  
  
  int num_bytes;
  
  uint8_t* buffer = NULL;
  
  const AVPixelFormat out_format = AV_PIX_FMT_RGB24;
  
  
  
  num_bytes = av_image_get_buffer_size(out_format, width, height, 1);
  
  buffer = (uint8_t*)av_malloc(num_bytes * sizeof(uint8_t));
  
  
  
  AVFrame* vid_frame = NULL;
  
  vid_frame = av_frame_alloc();
  
  AVFrame* conv_frame = NULL;
  
  conv_frame = av_frame_alloc();
  
  
  
  av_image_fill_arrays(conv_frame->data, conv_frame->linesize, buffer, 
  
                       out_format, width, height, 1);
  
  
  
  struct SwsContext *sws_ctx = NULL;
  
  sws_ctx = sws_getContext(width, height, cdc_ctx->pix_fmt,
  
                           width, height, out_format,
  
                           SWS_BILINEAR, NULL,NULL,NULL);
  
  
  
  int frame_num = 0;
  
  AVPacket vid_pckt;
  
  while (av_read_frame(fmt_ctx, &vid_pckt) >=0) {
  
      ret = avcodec_send_packet(cdc_ctx, &vid_pckt);
  
      if (ret < 0)
  
          break;
  
  
  
      ret = avcodec_receive_frame(cdc_ctx, vid_frame);
  
      if (ret < 0 && ret != AVERROR(EAGAIN) && ret != AVERROR_EOF)
  
          break;
  
      if (ret >= 0) {
  
          // convert image from native format to planar GBR
  
          sws_scale(sws_ctx, vid_frame->data, 
  
                    vid_frame->linesize, 0, vid_frame->height, 
  
                    conv_frame->data, conv_frame->linesize);
  
  
  
          unsigned char* r_ptr = output_arr + 
  
              (height * width * sizeof(unsigned char) * 3 * frame_num);
  
          unsigned char* g_ptr = r_ptr + (height * width * sizeof(unsigned char));
  
          unsigned char* b_ptr = g_ptr + (height * width * sizeof(unsigned char));
  
          unsigned int pxl_i = 0;
  
  
  
          for (unsigned int r = 0; r < height; ++r) {
  
              uint8_t* avframe_r = conv_frame->data[0] + r*conv_frame->linesize[0];
  
              for (unsigned int c = 0; c < width; ++c) {
  
                  r_ptr[pxl_i] = avframe_r[0];
  
                  g_ptr[pxl_i]   = avframe_r[1];
  
                  b_ptr[pxl_i]   = avframe_r[2];
  
                  avframe_r += 3;
  
                  ++pxl_i;
  
              }
  
          }
  
  
  
          ++frame_num;
  
  
  
          if (frame_num >= read_nframes)
  
              break;
  
      }
  
  }
  
  
  
  ...

  In my experience around two-thirds of the pixel values are different, each by +-1 (in a range of [0,255]). I am wondering is it due to some decoding scheme FFmpeg uses for reading JPEG frames ? I tried encoding and decoding png frames, and it works perfectly fine.

  In short my goal is to get the same pixel by pixel values for each JPEG frame as I would I have gotten if I was reading the JPEG images directly. Here is the stand-alone code I used. It includes cmake files to build code, and a couple of jpeg frames with the converted avi file to test this problem. (give —filetype png to test the png decoding).