Parallelizing an Image Compression Toolbox

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Parallelizing an Image Compression Toolbox

• MSE Project - Presentation 1
• Hadassa Baker

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Topics of Discussion

• Introduction
• Overview
• Requirements
• Methodology
• Image Toolbox Description
• Project Plan
• Cost Estimation
• SQA Plan

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Introduction

• The use of digital motion pictures is gaining
  much popularity in various industries, such as
  film production companies, museums, etc.
• Digital image files are used to create digital
  motion pictures.
• Digital image files are generally large and
  require compression to be used effectively.
• Image compression processes are generally
  computationally intensive

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Introduction

• To process 1hr of high definition video
• 1 frame 1920 pixels wide, 1080 pixels high, 3
  components/pixel (RGB) 6220.8 KB
• In general 24 frames/second
• Total number of frames per hour 24 fr/s 60
  s/min 60 min/hr 86400
• Total data size 86406220800 537477120000 B
  537477 MB/hr
• Need to speed up compression process

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Overview

• Purpose
• To explore the use of parallel programming
  techniques to speed up a computationally
  intensive image compression and decompression
  process

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Overview

• Goal
• To rewrite a sequential image compression toolbox
  source code into a parallel program in an effort
  to speed up the software
• Analyze factors that affect execution speed
  such as number of processors
• Look for general trends

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Requirement Specification

• The image toolbox is a sequential command-line
  program that takes a RAW image file as input,
  encodes it, and outputs a compressed encoded file
  .
• On the reverse, the image compression toolbox
  takes an encoded file as input, decodes it, and
  outputs a RAW image file

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Main Requirements

• The encoding and decoding processes of the image
  toolbox will be rewritten into a parallel program
  
• The RAW image reader and writer will be replaced
  with a Tiff image reader and writer
• Assessment will be made on the usefulness of
  parallel programming in speeding up the image
  compression toolbox

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Use Cases

• Use Case 1 Compressing an Image
• Description The user wants to compress a tiff
  file.
• Scenario The user runs the image compression
  console program to compress a tiff image. The
  user provides the name and path of a tiff image
  file as program input. The user provides a name
  and path for the compressed output file. The
  cmp extension is used for the compressed file.
  
• Specific Requirements-
• Correctness The compressed output file produced
  by the parallel program should be identical to
  the compressed output file produced with the
  sequential program.

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Use Cases

• Use Case 2 Decompressing an Image
• Description The user wants to decompress a
  compressed file into a tiff image file.
• Scenario The user runs the image compression
  console program to decompress a cmp file and
  write it out to a tiff file. The user provides
  the name and path of a compressed file as program
  input. The user also provides a name and path
  for the tiff file.
• Specific Requirements-
• Correctness The output tiff file produced by
  the parallel program should be exactly the same
  as the output tiff file produced with the
  sequential program.

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Methodology

• Described in Designing and Building Parallel
  Programs, by Ian Foster
• Structures the design process as four distinct
  stages partitioning, communication,
  agglomeration, and mapping.
• Partitioning the computation that should be
  operated on the data and the data to be operated
  on are decomposed into smaller takes

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Methodology

• Communication communication structures between
  tasks are defined for proper execution of program
  
• Agglomeration the outcome of the partitioning
  and the communication stages are evaluated
• Mapping each task is mapped to a processor in
  such a way that communication between tasks is
  decreased and execution is speeded up.

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Performance Modeling

• Goal
• Develop mathematical expressions that specify
  certain metrics as a function of problem size,
  number of processors, number of tasks, and other
  important characteristics.
• Performance models for
• Execution time the time that elapses from when
  the first processor starts executing on the
  problem to when the last processor completes
  execution.
• Parallel scalability - how algorithm performance
  varies with parameters such as problem size,
  processor count, number of tasks, and message
  startup cost.

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Image Compression Toolbox

• A wavelet based image compression tool, written
  by Satish Kumar.
• The source code was obtained from the internet.
• The program is written in C
• Permission is granted by the author to use the
  software for research purposes.
• Contains a collection of functions that are
  commonly used in wavelet based image compression
  techniques.

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Image Compression

• RAW image file read
• Four steps to the compression process

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Image Compression Toolbox

• Wavelet transformation
• Low frequency components of the data are
  separated from high frequency components of the
  data.
• On an image plane the low frequency components
  represent the base of the image, where small
  variation between neighboring coefficient exists.
  High frequency components represent areas where
  sharper differences between components exist.
• High pass and low pass filters are used on the
  image data first horizontally and then vertically
  to divide the frequency into two.
•  

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Image Compression Toolbox
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Image Compression Toolbox

• Optimal Bit Allocation
• Each class is allocated a portion of the total
  bit budget, such that the compressed image has
  the minimum possible distortion.
• The aim of bit allocation using rate-distortion
  techniques is meeting the requirement of overflow
  prevention while maximizing the image/video
  quality.

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Image Compression Toolbox

• Quantization
• The division of a quantity into discrete number
  of small parts, that are integral multiples of a
  common quantity.
• A scalar and uniform quantizer is used. Uniform
  where the levels are spaced equally, and scalar
  where each data is processed individually.

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Image Compression Toolbox

• Entropy Encoding
• Arithmetic encoding method used.
• Arithmetic coding takes a stream of input symbols
  and replaces it with a single number less than 1
  and greater than 0.
• The arithmetic coding process requires each input
  symbol to be encoded sequentially.

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Project Plan

• Initial Phase
• Develop overall requirements
• Documentation
• Vision document
• Project Plan
• Image Toolbox Description,
• SQA Plan
• Milestone - Presentation 1 - Get approval from
  committee and/or incorporate changes and
  suggestions

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Project Plan

• Architecture Phase
• Design architecture of the parallel program for
  the image toolbox
• Documentation
• Algorithm Design
• Parallel program design analysis
• Refine Vision document, Project Plan and SQA Plan
• Test Plan
• Milestone - Presentation 2 - Get approval from
  committee and/or incorporate changes and
  suggestions.

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Project Plan

• Implementation phase
• Implement parallel program
• Perform testing
• Documentation
•  Well documented source code
•  Test report
•  Test evaluation report
• Milestone - Presentation 3 - Get approval from
  committee and/or incorporate changes and
  suggestions.

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Project Plan

• Cost Estimation
• COCOMO - Organic model uncomplicated
• Person Months 2.4 KDSI1.05 
• KDSI Project size in thousands of delivered
  source instructions.
• Function point analysis best works with business
  type applications. Therefore, the size of the
  image compression toolbox is used as an estimate
• Approximately 1300 lines of source code in the
  image compression toolkit.
• Person month 3.2
• DURATION 2.5 EFFORT0.38 3.5 month
•  

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SQA Plan

• Tools
• Microsoft C 6.0
• Microsoft Visio
• Microsoft Word
• Deliverables
• Vision Document
• Project Plan Document
• SQA Plan Document
• Image Toolbox Description

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SQA Plan

• Deliverables (cont)
• Architecture Design Document
• Test Plan Document
• Image Toolbox sequential source code.
• Image Toolbox parallel source code.
• Test Report Document
• Test Evaluation Report Document