Image Processing with QccPack and Python
Limited bandwidth and storage space are always a challenge. Data compression is often the best solution. When it comes to image processing, compression techniques are divided into two types: lossless and lossy data compression.
QccPack, developed by James Fowler, is an open-source collection of library routines and utility programs for quantization and reliable implementation of common compression techniques.
Libraries written for QccPack have a clean interface. So far, these libraries can be upgraded without having to modify the application code. QccPack consists of a static-linked library, libQccPack.a, and supports dynamic linking with libQccPack.so.
Entropy coding, wavelet transforms, wavelet-based sub-band coding, error coding, image processing and implementations of general routines can be done through the library routines available with QccPack. Optional modules are available for the QccPack library that you can add later. QccPackSPIHT is one optional module for the QccPack library that provides an implementation of the Set Partitioning in Hierarchical Trees (SPIHT) algorithm for image compression. The QccPackSPIHT module includes two utility executables, spihtencode and spihtdecode, to perform SPIHT encoding and decoding for grayscale images.
QccPack and QccPackSPIHT are available for download from the QccPack Web page on SourceForge. Red Hat users can find source and binary RPMs at that Web site. Users of other systems will need to compile the source code. QccPack has been complied successfully on Solaris/SPARC, Irix, HP-UX, Digital UNIX Alpha and Digital RISC/Ultrix.
You can use QccPack to train a VQ codebook on an image and then to code the image with full-search VQ followed with arithmetic coding. Take a 512*512 grayscale Lenna image, for example. The following sample procedure assumes you are at the Python interpreter prompt.
Step 1: convert from the PGM image file format to the DAT format file by extracting four-dimensional (2x2) vectors of pixels:
>>> imgtodat-ts 4 lenna.pgm.gz lenna.4D.dat.gz
Step 2: train a 256-codeword VQ codebook on the DAT file with GLA (stopping threshold = 0.01):
>>> gla -s 256 -t 0.01 lenna.4D.dat.gz lenna.4D256.cbk
Step 3: vector quantize the DAT file to produce a channel of VQ indices:
>>> vqencode lenna.4D.dat.gz lenna.4D256.cbk lenna.vq.4D256.chn
Step 4: calculate first-order entropy of VQ indices (as bits/pixel):
>>> chnentropy -d 4 lenna.vq.4D256.chn First-order entropy ↪of channel lenna.vq.4D256.chn is: 1.852505 (bits/symbol)
Step 5: arithmetic-encode channel of VQ indices:
>>> chnarithmeticencode -d 4 lenna.vq.4D256.chn ↪lenna.vq.4D256.chn.ac
Channel lenna.vq.4D256.chn arithmetic coded to: 1.830322 (bits/symbol):
>>> rm lenna.vq.4D256.chn
Step 6: decode arithmetic-coded channel:
>>> chnarithmeticdecode lenna.vq.4D256.chn.ac lenna.vq.4D256.chn
Step 7: inverse VQ channel to produce quantized data:
>>> vqdecode lenna.vq.4D256.chn lenna.4D256.cbk ↪lenna.vq.4D256.dat.gz
Step 8: convert from DAT to PGM format:
>>> dattoimg 512 512 lenna.vq.4D256.dat.gz lenna.vq.4D256.pgm
Step 9: calculate distortion between original and coded images:
>>> imgdist lenna.pgm.gz lenna.vq.4D2 56.pgm
The distortion between files lenna.pgm.gz and lenna.vq.4D256.pgm is:
22.186606 dB (SNR)
36.719100 dB (PSNR)
The Python Imaging Library adds image processing capabilities to the Python interpreter. This library provides extensive file format support, an efficient internal representation and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. The library contains some basic image processing functionality, including point operations, filtering with a set of built-in convolution kernels and color space conversions. The Python Imaging Library is ideal for image archival and batch processing applications. You can use the library to create thumbnails, convert between file formats and print images. The library also supports image resizing, rotation and arbitrary affine transforms.
The Python Imaging Library uses a plugin model that allows you to add your own decoders to the library, without any changes to the library itself. These plugins have names such as XxxImagePlugin.py, where Xxx is a unique format name (usually an abbreviation).
Practical Task Scheduling Deployment
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide