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 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 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 legal regulation 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 Vertical View Horizontal View Basics(4)

Normal viewHorizontal viewVertical view Different views on the video block Basics(5) Frame 40Frame 112Frame 112

Scan order Basics(6)

File seen as a stream of gray values Written 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“ 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 Motion compensation – Block matching Basics(8)

Usage in lossless coding Normally 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 block  Cut normally, vertically and horizontally  With or without blockmatching  Frame based or stream based computing Implemented in c++ Implementation Realization(1)

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 Implementation of block matching Realization(2)

JPEG 2000 Lossless mode Java Implementation: JJ2000 ( Standard options except:  Lossless Mode (“ –lossless on “)  Cancel console output (“ –verbose off “) Lossless frame compression Realization(3)

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 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