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Telematics/Network Engineering. Experimental study on scan order and motion compensation in lossless video coding. Scan order and motion compensation in lossless coding. Team. School of Telematics and Network Engineering Carinthia Tech Institute, Austria

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Telematics/Network Engineering

Experimental study on scan order and motion compensation in lossless video coding


Scan order and motion compensation in lossless coding
Scan order and motion compensation in lossless coding

Team

School of Telematics and Network Engineering

Carinthia Tech Institute, Austria

  • Team of students: Stefan A. Kramatsch

    Agnes Gruber, Alexander Krapesch, Stefan Matschitsch, Thomas Mayerdorfer,

    Stefan Miedl, Stefan Moser, Martin Tschinder, Stefan Zorn-Pauli

  • Project leader

    Dr. Andreas Uhl

  • Head of School

    Dr. Herbert Stögner


Presentation Outline

Structure

  • Motivation

  • Basics

  • Realization

  • Results

  • Conclusion


Motivation

Project goals

  • Semester Project in Compression Techniques 2

  • Alternative way to view videos

  • Make data compression more concrete

  • Experience usage of programming languages in

    picture processing


Basics(1)

Lossless video coding

  • Mainly used in medical applications – required by legal

    regulation

  • JPEG, JPEG-LS, lossless JPEG 2000 on per-frame basis

  • Temporal redundancy ignored

     no motion compensation

     limited compression performance


Basics(2)

Classical view of video data


Basics(3)

Classical view of video data

  • Temporally ordered still images

  • Frames are similar

     basis for motion compensated hybrid coding

     basis for application of 3D video techniques

  • Possible to form a 3D block of video data


Vertical View

Horizontal View

Normal View

Basics(4)

Different views on the video block


Basics(5)

Different views on the video block

Normal view Horizontal view Vertical view

Frame 40 Frame 112 Frame 112


Basics(6)

Scan order


Basics(7)

Streams – stream compression

  • File seen as a stream of gray values

  • Written to a .txt file

  • File compressors used:

    - Arithmetical coder

    - Runlength Encoding (RLE)

    - Huffman Coding


Basics(8)

Motion compensation – Block matching

  • Scene divided into non-overlapping “block“ regions

  • Compare blocks (current <-> reference frame)

     motion vector for each block

  • “Best“ match based on mean square error

     Stored as prediction

  • Current frame – prediction = residual frame

    • to be compressed

    • Common for lossy compression


Basics(9)

Motion compensation – Block matching

  • Usage in lossless coding

  • Normally temporal based

     now spatially

Reference Frame 1 Residual Frame 40

Vertical View

Horizontal View

Frame 112 non BM and BM

Frame 112 non BM and BM


Realization(1)

Implementation

  • Input: all frames of a video (in .pgm format)

  • Build the 3D video block

    • Cut normally, vertically and horizontally

    • With or without blockmatching

    • Frame based or stream based computing

  • Implemented in c++


Realization(2)

Implementation of block matching

  • Matlab application

  • Based on one reference frame

    • all remaining: residual frames

  • Searchwindow 32x32 Pixels

  • Blocksize 16x16 Pixels

  • Similar Block search based on Root Mean Square


Realization(3)

Lossless frame compression

  • JPEG 2000 Lossless mode

  • Java Implementation: JJ2000 (http://jj2000.epfl.ch)

  • Standard options except:

    • Lossless Mode (“ –lossless on “)

    • Cancel console output (“ –verbose off “)


Realization(4)

Testvideos (Spatial x Temporal resolution)

  • Akiyo (176 x 144 x 300) – low movement

  • Carphone (176 x 144 x 383) – high movement

  • Claire (176 x 144 x 494) – low movement

  • Football (720 x 486 x 60) – high movement

  • Foreman (176 x 144 x 49) – high movement

  • Grandma (176 x 144 x 871) – low movement

  • Mobile (720 x 576 x 40) – high movement

  • Mother and Daughter (176 x 144 x 962) – low movement

  • Salesman (176 x 144 x 449) – low movement


Results

Compression Ratio

Low movementHigh MovementStream


Conclusion(1)

Without Blockmatching

  • Improved frame based compression by alternative views

  • Exploitation of spatial instead of temporal redundancies through alternative scan order

  • Little computational demand compared to BM

  • Increased memory demand and coding delay

  • Stream compression has little effect


Conclusion(2)

With Blockmatching

  • The increase of compression ratio does not justify the usage of BM algorithms in case of alternative views

  • Superior results for 1D based compression algorithms


Telematics/Network Engineering

Thank you for your attention!


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