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• Revision 29188 : 2 options de plus pour personnaliser la page d’activation de la mutu : * ...

  15 juin 2009, par real3t@… — Log

  2 options de plus pour personnaliser la page d’activation de la mutu :
  * ’branding’ : texte libre en HTML
  * ’branding_logo’ => logo (sous forme de HTML)

• Is it possible to read all files of the same format into one command in ffmpeg ?

  17 juillet 2021, par DataMsc

  I have a python script which downloads a number of .ts and .aac files in the current working directory. I want to be able to concatenate them individually and then combine the final .ts and .aac files to produce the output.

  


  Currently, I have around 90 files for both ts and aac. I understand it is possible to create a batch file containing the entirety of the related files which can be used to join them together, however, I wanted to find a solution that can ignore the creation of a batch file to increase the speed of the script.

  


  Here is a basic interpretation of my request :

  


  


  • 1.ts, 2.ts, 3.ts, 4.ts, 5.ts, 6.ts

    


    ffmpeg -f concat -safe 0 -i 1.ts:6.ts -c copy output.ts

    


  


  


  I've tried surfing the web for a solution, but all results lead me back to a batch file.
Any help is appreciated

  


• Why JPEG compressing an uncompressed image differs its original (FFmpeg, NvJPEG, ...)

  22 juin 2021, par Fruchtzwerg

  I am currently struggling to understand why recompressing an uncompressed JPEG image differs its original.

  


  It's clear, that JPEG is a lossy compression, but what if the image to compress is already uncompressed, which means all sampling losses are already included ? In other words : Downsampling and DCT should be inversable at this point without loosing data.

  


  

  


  To make sure losses are not effected by the color space conversion, this step is skipped and YUV images are used.

  


  

  1. Compress YUV image to JPEG (image.yuv —> image.yuv.jpg)

  


  2. Uncompress JPEG image to YUV image (image.yuv.jpg —> image.yuv.jpg.yuv)

  


  3. Compress YUV image to JPEG (image.yuv.jpg.yuv —> image.yuv.jpg.yuv.jpg)

  


  4. Uncompress JPEG image to YUV image (image.yuv.jpg.yuv.jpg —> image.yuv.jpg.yuv.jpg.yuv)

  


  


  Step 1 includes a lossy compression, so we will not deal with this step anymore. For me, intresting is what happens afterwards :

  


  Uncompressing the JPEG image back to YUV (step 2) leads to an image which perfectly fits all sampling steps if compressed again (step 3). So the JPEG image after step 3 should (from my understanding) be exactly the same as after step 1. Also the YUV images after step 4 and step 2 should equal each other.

  


  Looking at the steps for one 8x8 block the following simplified sequence should illustrate what I am trying to descibe. Lets start with the original YUV image, which can only be decompressed loosing all decimal places :

  


  [ 1.123, 2.345, 3.456, ... ]    (YUV)
    DTC + Quantization
[ -26, -3, -6, ... ]            (Quantized frequency space)
    Inverse DTC + Quantization
[ 1, 2, 3, ... ]                (YUV)


  


  Doing this with input, which already matches all steps, which may lead to loss of data afterwards (using round numbers in my example), the decompressed image should match its original :

  


  [ 1, 2, 3, ... ]                (YUV)
    DTC + Quantization
[ -26, -3, -6, ... ]            (Quantized frequency space)
    Inverse DTC + Quantization
[ 1, 2, 3, ... ]                (YUV)


  


  There are also some sources and discussions, which are confirming my idea :

  


  

  • need help creating Jpeg Generational Degradation code

  


  • What factors cause or prevent “generational loss” when JPEGs are recompressed multiple times ?

  


  • Lossless Chroma Subampling

  


  


  So much for theory. In praxis, I've runned these steps using ffmpeg and Nvidias jpeg samples (using NvJPEGEncoder).

  


  ffmpeg :

  


  #Create YUV image
ffmpeg -y -i image.jpg -s 1920x1080 -pix_fmt yuv420p image.yuv
#YUV to JPEG
ffmpeg -y -s 1920x1080 -pix_fmt yuv420p -i image.yuv image.yuv.jpg
#JPEG TO YUV
ffmpeg -y -i image.yuv.jpg -s 1920x1080 -pix_fmt yuv420p image.yuv.jpg.yuv
#YUV to JPEG
ffmpeg -y -s 1920x1080 -pix_fmt yuv420p -i image.yuv.jpg.yuv image.yuv.jpg.yuv.jpg
#JPEG TO YUV
ffmpeg -y -i image.yuv.jpg.yuv.jpg -s 1920x1080 -pix_fmt yuv420p image.yuv.jpg.yuv.jpg.yuv
#YUV to JPEG
ffmpeg -y -s 1920x1080 -pix_fmt yuv420p -i image.yuv.jpg.yuv.jpg.yuv image.yuv.jpg.yuv.jpg.yuv.jpg


  


  Nvidia :

  


  #Create YUV image
./jpeg_decode num_files 1 image.jpg image.yuv
#YUV to JPEG
./jpeg_encode image.yuv 1920 1080 image.yuv.jpg
#JPEG TO YUV
./jpeg_decode num_files 1 image.yuv.jpg image.yuv.jpg.yuv
#YUV to JPEG
./jpeg_encode image.yuv.jpg.yuv 1920 1080 image.yuv.jpg.yuv.jpg
#JPEG TO YUV
./jpeg_decode num_files 1 image.yuv.jpg.yuv.jpg image.yuv.jpg.yuv.jpg.yuv
#YUV to JPEG
./jpeg_encode image.yuv.jpg.yuv.jpg.yuv 1920 1080 image.yuv.jpg.yuv.jpg.yuv.jpg


  


  But a comparison of the images

  


  

  • image.yuv.jpg.yuv and image.yuv.jpg.yuv.jpg.yuv

  


  • image.yuv.jpg.yuv.jpg and image.yuv.jpg.yuv.jpg.yuv.jpg

  


  


  showing differences in the files. That brings me to my question why and where the difference gets happen, since from my understanding the files should be equal.