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• Creating a reflection

  9 juin 2010, par Mikko Koppanen — Imagick, PHP stuff

  Here is a simple example of creating a reflection of an image. The reflection is created by flipping the image and overlaying a gradient on it. Then both, the original image and the reflection is overlayed on a canvas.

  This example is created for Imagick 2.1.x but with a little tuning it should work with earlier versions.

  php

  < view plain text >

  1. < ?php
  2.  
  3. /* Read the image */
  4. $im = new Imagick( "strawberry.png" ) ;
  5.  
  6. /* Thumbnail the image */
  7. $im->thumbnailImage( 200, null ) ;
  8.  
  9. /* Create a border for the image */
  10. $im->borderImage( "white", 5, 5 ) ;
  11.  
  12. /* Clone the image and flip it */
  13. $reflection = $im->clone() ;
  14. $reflection->flipImage() ;
  15.  
  16. /* Create gradient. It will be overlayd on the reflection */
  17. $gradient = new Imagick() ;
  18.  
  19. /* Gradient needs to be large enough for the image
  20. and the borders */
  21. $gradient->newPseudoImage( $reflection->getImageWidth() + 10,
  22.               $reflection->getImageHeight() + 10,
  23.               "gradient:transparent-black"
  24.             ) ;
  25.  
  26. /* Composite the gradient on the reflection */
  27. $reflection->compositeImage( $gradient, imagick: :COMPOSITE_OVER, 0, 0 ) ;
  28.  
  29. /* Add some opacity */
  30. $reflection->setImageOpacity( 0.3 ) ;
  31.  
  32. /* Create empty canvas */
  33. $canvas = new Imagick() ;
  34.  
  35. /* Canvas needs to be large enough to hold the both images */
  36. $width = $im->getImageWidth() + 40 ;
  37. $height = ( $im->getImageHeight() * 2 ) + 30 ;
  38. $canvas->newImage( $width, $height, "black", "png" ) ;
  39.  
  40. /* Composite the original image and the reflection on the canvas */
  41. $canvas->compositeImage( $im, imagick: :COMPOSITE_OVER, 20, 10 ) ;
  42. $canvas->compositeImage( $reflection, imagick: :COMPOSITE_OVER,
  43.             20, $im->getImageHeight() + 10 ) ;
  44.  
  45. /* Output the image*/
  46. header( "Content-Type : image/png" ) ;
  47. echo $canvas ;
  48.  
  49.  ?>

  The source image :

  

  And the result :

  

  P.S. Please send me some new images which I can use in these examples

• Multiprocess FATE Revisited

  26 juin 2010, par Multimedia Mike — FATE Server, Python

  I thought I had brainstormed a simple, elegant, multithreaded, deadlock-free refactoring for FATE in a previous post. However, I sort of glossed over the test ordering logic which I had not yet prototyped. The grim, possibly deadlock-afflicted reality is that the main thread needs to be notified as tests are completed. So, the main thread sends test specs through a queue to be executed by n tester threads and those threads send results to a results aggregator thread. Additionally, the results aggregator will need to send completed test IDs back to the main thread.

  
  
  

  But when I step back and look at the graph, I can’t rationalize why there should be a separate results aggregator thread. That was added to cut down on deadlock possibilities since the main thread and the tester threads would not be waiting for data from each other. Now that I’ve come to terms with the fact that the main and the testers need to exchange data in realtime, I think I can safely eliminate the result thread. Adding more threads is not the best way to guard against race conditions and deadlocks. Ask xine.

  
  
  

  I’m still hung up on the deadlock issue. I have these queues through which the threads communicate. At issue is the fact that they can cause a thread to block when inserting an item if the queue is "full". How full is full ? Immaterial ; seeking to answer such a question is not how you guard against race conditions. Rather, it seems to me that one side should be doing non-blocking queue operations.

  This is how I’m planning to revise the logic in the main thread :

  test_set = set of all tests to execute
  tests_pending = test_set
  tests_blocked = empty set
  tests_queue = multi-consumer queue to send test specs to tester threads
  results_queue = multi-producer queue through which tester threads send results
  while there are tests in tests_pending :
    pop a test from test_set
    if test depends on any tests that appear in tests_pending :
      add test to tests_blocked
    else :
      add test to tests_queue in a non-blocking manner
      if tests_queue is full, add test to tests_blocked
    while there are results in the results_queue :
  
      get a result from result_queue in non-blocking manner
  
      remove the corresponding test from tests_pending
  if tests_blocked is non-empty :
  
    sleep for 1 second
  
    test_set = tests_blocked
  
    tests_blocked = empty set
  
  else :
  
    insert n shutdown signals, one from each thread
  go to the top of the loop and repeat until there are no more tests
  while there are results in the results_queue :
  
    get a result from result_queue in a blocking manner
  

  Not mentioned in the pseudocode (so it doesn’t get too verbose) is logic to check whether the retrieved test result is actually an end-of-thread signal. These are accounted and the whole test process is done when one is received for each thread.

  On the tester thread side, it’s safe for them to do blocking test queue retrievals and blocking result queue insertions. The reason for the 1-second delay before resetting tests_blocked and looping again is because I want to guard against the situation where tests A and B are to be run, A depends of B running first, and while B is running (and happens to be a long encoding test), the main thread is spinning about, obsessively testing whether it’s time to insert A into the tests queue.

  It all sounds just crazy enough to work. In fact, I coded it up and it does work, sort of. The queue gets blocked pretty quickly. Instead of sleeping, I decided it’s better to perform the put operation using a 1-second timeout.

  Still, I’m paranoid about the precise operation of the IPC queue mechanism at work here. What happens if I try to stuff in a test spec that’s a bit too large ? Will the module take whatever I give it and serialize it through the queue as soon as it can ? I think an impromptu science project is in order.

  big-queue.py :

  PLAIN TEXT

  PYTHON :

  1. # !/usr/bin/python
  2.  
  3. import multiprocessing
  4. import Queue
  5.  
  6. def f(q) :
  7.   str = q.get()
  8.   print "reader function got a string of %d characters" % (len(str))
  9.  
  10. q = multiprocessing.Queue()
  11. p = multiprocessing.Process(target=f, args=(q,))
  12. p.start()
  13. try :
  14.   q.put_nowait(’a’ * 100000000)
  15. except Queue.Full :
  16.   print "queue full"

  $ ./big-queue.py
  reader function got a string of 100000000 characters
  

  Since 100 MB doesn’t even make it choke, FATE’s little test specs shouldn’t pose any difficulty.

• The problems with wavelets

  27 février 2010, par Dark Shikari — DCT, Dirac, Snow, psychovisual optimizations, wavelets

  I have periodically noted in this blog and elsewhere various problems with wavelet compression, but many readers have requested that I write a more detailed post about it, so here it is.

  Wavelets have been researched for quite some time as a replacement for the standard discrete cosine transform used in most modern video compression. Their methodology is basically opposite : each coefficient in a DCT represents a constant pattern applied to the whole block, while each coefficient in a wavelet transform represents a single, localized pattern applied to a section of the block. Accordingly, wavelet transforms are usually very large with the intention of taking advantage of large-scale redundancy in an image. DCTs are usually quite small and are intended to cover areas of roughly uniform patterns and complexity.

  Both are complete transforms, offering equally accurate frequency-domain representations of pixel data. I won’t go into the mathematical details of each here ; the real question is whether one offers better compression opportunities for real-world video.

  DCT transforms, though it isn’t mathematically required, are usually found as block transforms, handling a single sharp-edged block of data. Accordingly, they usually need a deblocking filter to smooth the edges between DCT blocks. Wavelet transforms typically overlap, avoiding such a need. But because wavelets don’t cover a sharp-edged block of data, they don’t compress well when the predicted data is in the form of blocks.

  Thus motion compensation is usually performed as overlapped-block motion compensation (OBMC), in which every pixel is calculated by performing the motion compensation of a number of blocks and averaging the result based on the distance of those blocks from the current pixel. Another option, which can be combined with OBMC, is “mesh MC“, where every pixel gets its own motion vector, which is a weighted average of the closest nearby motion vectors. The end result of either is the elimination of sharp edges between blocks and better prediction, at the cost of greatly increased CPU requirements. For an overlap factor of 2, it’s 4 times the amount of motion compensation, plus the averaging step. With mesh MC, it’s even worse, with SIMD optimizations becoming nearly impossible.

  At this point, it would seem wavelets would have pretty big advantages : when used with OBMC, they have better inter prediction, eliminate the need for deblocking, and take advantage of larger-scale correlations. Why then hasn’t everyone switched over to wavelets then ? Dirac and Snow offer modern implementations. Yet despite decades of research, wavelets have consistently disappointed for image and video compression. It turns out there are a lot of serious practical issues with wavelets, many of which are open problems.

  1. No known method exists for efficient intra coding. H.264′s spatial intra prediction is extraordinarily powerful, but relies on knowing the exact decoded pixels to the top and left of the current block. Since there is no such boundary in overlapped-wavelet coding, such prediction is impossible. Newer intra prediction methods, such as markov-chain intra prediction, also seem to require an H.264-like situation with exactly-known neighboring pixels. Intra coding in wavelets is in the same state that DCT intra coding was in 20 years ago : the best known method was to simply transform the block with no prediction at all besides DC. NB : as described by Pengvado in the comments, the switching between inter and intra coding is potentially even more costly than the inefficient intra coding.

  2. Mixing partition sizes has serious practical problems. Because the overlap between two motion partitions depends on the partitions’ size, mixing block sizes becomes quite difficult to define. While in H.264 an smaller partition always gives equal or better compression than a larger one when one ignores the extra overhead, it is actually possible for a larger partition to win when using OBMC due to the larger overlap. All of this makes both the problem of defining the result of mixed block sizes and making decisions about them very difficult.

  Both Snow and Dirac offer variable block size, but the overlap amount is constant ; larger blocks serve only to save bits on motion vectors, not offer better overlap characteristics.

  3. Lack of spatial adaptive quantization. As shown in x264 with VAQ, and correspondingly in HCEnc’s implementation and Theora’s recent implementation, spatial adaptive quantization has staggeringly impressive (before, after) effects on visual quality. Only Dirac seems to have such a feature, and the encoder doesn’t even use it. No other wavelet formats (Snow, JPEG2K, etc) seem to have such a feature. This results in serious blurring problems in areas with subtle texture (as in the comparison below).

  4. Wavelets don’t seem to code visual energy effectively. Remember that a single coefficient in a DCT represents a pattern which applies across an entire block : this makes it very easy to create apparent “detail” with a DCT. Furthermore, the sharp edges of DCT blocks, despite being an apparent weakness, often result in a “fake sharpness” that can actually improve the visual appearance of videos, as was seen with Xvid. Thus wavelet codecs have a tendency to look much blurrier than DCT-based codecs, but since PSNR likes blur, this is often seen as a benefit during video compression research. Some of the consequences of these factors can be seen in this comparison ; somewhat outdated and not general-case, but which very effectively shows the difference in how wavelets handle sharp edges and subtle textures.

  Another problem that periodically crops up is the visual aliasing that tends to be associated with wavelets at lower bitrates. Standard wavelets effectively consist of a recursive function that upscales the coefficients coded by the previous level by a factor of 2 and then adds a new set of coefficients. If the upscaling algorithm is naive — as it often is, for the sake of speed — the result can look quite ugly, as if parts of the image were coded at a lower resolution and then badly scaled up. Of course, it looks like that because they were coded at a lower resolution and then badly scaled up.

  JPEG2000 is a classic example of wavelet failure : despite having more advanced entropy coding, being designed much later than JPEG, being much more computationally intensive, and having much better PSNR, comparisons have consistently shown it to be visually worse than JPEG at sane filesizes. Here’s an example from Wikipedia. By comparison, H.264′s intra coding, when used for still image compression, can beat JPEG by a factor of 2 or more (I’ll make a post on this later). With the various advancements in DCT intra coding since H.264, I suspect that a state-of-the-art DCT compressor could win by an even larger factor.

  Despite the promised benefits of wavelets, a wavelet encoder even close to competitive with x264 has yet to be created. With some tests even showing Dirac losing to Theora in visual comparisons, it’s clear that many problems remain to be solved before wavelets can eliminate the ugliness of block-based transforms once and for all.