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Sketchs characterization and compression for nomadic computing
Nelson Baloian, Ramón Cruzat, Richard Ibarra Departamento de Ciencias de la Computación Universidad de Chile Javier Bustos-Jiménez ORAND
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Motivation Mobile computing: Learning Design Planning Geology
Topography Engeneering
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Our Experience Mobile collaborative applications P2P architechture
Based on sketching & gesturing
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The problem sketch Sketching originates lots of data
Must be shared in real time Mobile devices have low memory & processig capacity sketch
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The solution Reduce data How ? But this is not new !
Characterizing and “compressing” sketches But this is not new ! Jones, Durand & Desbrun Lee et al. Ramer, Douglas,Peucker They still demad too much computation power Results are not satisfactory for the mobile scenario
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Our proposal: Two new algorithms
Distance-based algorithm. If distance AB is less than e point B can be discarded Area-based algorithm. Areas covered by trapezoid ABCX and ACX are compared to decide if B does provide additional information
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Evaluation (1) Evaluation parameters
Time for characterizing & compressing sketch (t). Size of resulting characterization (s). % of information loosing due the compression (l). calculated as ||O − R||/||O||, O is the pixel matrix of the original image and R is the pixel matrix of the reconstructed image
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Evaluation (2) Compare against what ?
Algoritms from the literature already discarted Strightforward algorothms: Image algorithm take a photo of the sketch and scale it, for reducing the size of the image. Compression algorithm use the JavaScript Objection Notation (JSON) for representing the data as an array of pairs [[20.7, 10.2], [8.3, 15.4], ...] and zip it
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Evaluation tool: X application
Java application for freehand sketching Transformation algorithms as plug-ins Generates statistics
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Statistics: HTML file
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Experiments - 1 A basic Sketch: rectangle Processed by 4 algoritms
Area with e = 1, 100, 1000 square pixels Distance with e = 1, 100 and 1000 pixels Scaling Coding and zipping
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Experiments - 2 A basic Sketch: cross Processed by 4 algoritms
Area with e = 1, 100, 1000 square pixels Distance with e = 1, 100 and 1000 pixels Scaling Coding and zipping
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Experiments - 3 Advanced Sketch: figure Processed by 4 algoritms
Area with e = 1, 100, 1000 square pixels Distance with e = 1, 100 and 1000 pixels Scaling Coding and zipping
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Experiments - 4 Advanced Sketch: handwriting Processed by 4 algoritms
Area with e = 1, 100, 1000 square pixels Distance with e = 1, 100 and 1000 pixels Scaling Coding and zipping
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Results: mean values basic sketches
Algorithm Time [msec] Mean size [kb] % Area e=1.0 0.47 0.8 < 1% Area e=100.0 0.18 0.10 5-6% Area e=1000.0 0.06 0.03 > 24% Distance e=1.0 0.35 1.0 0.02% Distance e=10.0 0.21 0.39 Distance e=100.0 Figure was lost Scaling to 25% 0.53 18.75 7.5% ZIP 0.67 0.6 --- RDP Algorithm 2.1 1.39 5 % Original 3.4 75
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Mean values for advanced sketches
Algorithms Time [msec] Mean size [kb] % Area e=1.0 0.58 2.2 < 1% Area e=100.0 0.28 0.35 5% Area e=1000.0 0.06 0.11 > 40% Distance e=1.0 0.33 3.5 0.02% Distance e=10.0 0.23 0.72 0.04% Distance e=100.0 Figure was lost Scaling to 25% 0.93 18.75 7.5% ZIP 0.98 1.74 --- RDP Algorithm 2.4 2.69 20% Original 3.4 75
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Subjective evaluation
How does the human user percieves the lose of information
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Conclusions It does pay off !
Results are valid for the current state of the art -> with more mobile computing power, other algoritms can be evaluated A methodology for evaluating how good are compression algorithms Combined with other mechanisms
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