Experimental study on scan order and motion compensation in lossless

video coding

Telematics/Network Engineering

Scan order and motion compensation in lossless coding

School of Telematics and Network EngineeringCarinthia Tech Institute, Austria

Team of students: Stefan A. KramatschAgnes 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

Team

Motivation

Basics

Realization

Results

Conclusion

Structure

Presentation Outline

Semester Project in Compression Techniques 2

Alternative way to view videos

Make data compression more concrete

Experience usage of programming languages in picture processing

Project goals

Motivation

Mainly used in medical applications – required by legalregulation

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

Temporal redundancy ignored no motion compensation limited compression performance

Lossless video coding

Basics(1)

Classical view of video data

Basics(2)

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

Classical view of video data

Basics(3)

Different views on the video block

Normal

View

Ver

tical

Vie

w

Horizontal View

Basics(4)

Normal view Horizontal view Vertical view

Different views on the video block

Basics(5)

Frame 40 Frame 112 Frame 112

Scan order

Basics(6)

File seen as a stream of gray valuesWritten to a .txt file

File compressors used:- Arithmetical coder- Runlength Encoding (RLE)- Huffman Coding

Streams – stream compression

Basics(7)

Scene divided into non-overlapping “block“ regionsCompare 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 compressedCommon for lossy compression

Motion compensation – Block matching

Basics(8)

Usage in lossless codingNormally temporal based now spatially

Motion compensation – Block matching

Basics(9)

Reference Frame 1 Residual Frame 40

Frame 112 non BM and BM

Horizontal View Vertical View

Frame 112 non BM and BM

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

Build the 3D video blockCut normally, vertically and horizontallyWith or without blockmatchingFrame based or stream based computing

Implemented in c++

Implementation

Realization(1)

Matlab application

Based on one reference frame all remaining: residual frames

Searchwindow 32x32 PixelsBlocksize 16x16 PixelsSimilar Block search based on Root Mean Square

Implementation of block matching

Realization(2)

JPEG 2000 Lossless mode

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

Standard options except:Lossless Mode (“ –lossless on “)Cancel console output (“ –verbose off “)

Lossless frame compression

Realization(3)

Akiyo (176 x 144 x 300) – low movementCarphone (176 x 144 x 383) – high movementClaire (176 x 144 x 494) – low movementFootball (720 x 486 x 60) – high movementForeman (176 x 144 x 49) – high movementGrandma (176 x 144 x 871) – low movementMobile (720 x 576 x 40) – high movementMother and Daughter (176 x 144 x 962) – low movementSalesman (176 x 144 x 449) – low movement

Testvideos (Spatial x Temporal resolution)

Realization(4)

Compression Ratio

Results

Low movement High Movement Stream

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

Without Blockmatching

Conclusion(1)

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

With Blockmatching

Conclusion(2)

Thank you for your attention!

Telematics/Network Engineering