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Content Based Compression

Content Based Compression

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Content Based Compression

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  1. Dr. Margaret Varga Defence Evaluation & Research Agency Malvern Worcestershire WR14 3PS UK Image Processing and Interpretation Click to add sub-title Telephone: +44 1684 895712 Facsimile: +44 1684 894384 Email: varga@signal.dera.gov.uk Content Based Compression

  2. Introduction • Huge volumes of images, video are collected: • e.g. Infra-red, optical, SAR, sonar, military exercise log book • and used: • surveillance, monitoring, mission assessment • Different characteristics: scale, textural, resolution etc. • Require large storage or efficient transmission • Need for fast, cost effective and reliable transmission, storage and retrieval

  3. Current Image Compression Techniques Problems • Only concern with compression ratio • Do not address the problems: • preserving relevant information • removing redundant data • Assessing such decompressed images, e.g. ATR -> unpredictable results • Lossless still used - images that required detail analysis and/or further processing

  4. Preservation of Information • In some applications • Local detail is crucial • Can not be coded away without changing the meaning and significance of the image • Small targets in surveillance imagery • Military activity assessment • Mission assessment • …...

  5. Cueing Process • Cueing - target detection and motion tracking • Maximise the detection of: • 'True +', i.e. real targets for which lossless (or near lossless) compression must be used; • 'True - ', i.e. real redundant areas for which lossy compression can be used; • Based on the photographic interpreters’ and intelligence analysts’ annotations • Minimise: • all the 'False +' and 'False -' , i.e. mistaken targets and background • Provide essential and reliable guidance for the application: • lossless compression techniques intelligently on the regions/targets of interests • lossy compression techniques non-relevant or background areas

  6. Cueing Process Still Imagery • Manual Annotation • Quadtree Based Cueing • Phase Congruency Video • Motion Surveillance

  7. Image Fusion Under-exposed Over-exposed Fused

  8. Image Fusion Raw Optical Raw SAR Fused

  9. Manual Annotation • Simple and could easily be performed using some form of a graphical user interface (GUI) • Form part of a system in which an intelligence analysts or photographic interpreter: • could interactively annotate imagery to mark out ROI • then being compressed intelligently prior to dispatch

  10. Manual Annotation An outline drawn around one particular area of interest in an image

  11. Quadtree • The Quadtree has been used in compression for many years • Its use for target detection is novel • The technique consists of decomposing an image into sub-images based on some criteria: • grey level similarity, image mean, variance etc. • If a region of an image: • is described satisfactorily by the chosen criteria then that region is left unmodified • otherwise it is decomposed into 4 sub-regions each of equal size. • The process continues until • no further decomposition is carried out or • some minimum region size has been reached

  12. HV Quadtree Parsing

  13. Quadtree Standard quadtree HV quatree The HV-quadtree gives an improved representation of an image yielding in some case up to 75% less regions. The fine resolution areas of the grid form the masks for ROI

  14. Phase Congruency • All image features have in common in the Fourier domain frequency components over a wide range - maximal in phase congruency • The angle at which this phase-congruency occurs is characteristic of the type of feature • For example: • +ve step = 0 • -ve step =  • +ve ridge = /2 • -ve ridge = 3/2 • A feature could be defined as the location at which there is a congruence of phase • It is invariant to contrast in a feature

  15. Phase Congruency The antennae together with their shadows particularly those in the distance are clearly extracted.

  16. Model Based Motion Tracking • Automatic recognition and tracking of vehicles in video sequences from fixed surveillance cameras • The technique: • fitting 2-D vehicle models to images • and then track the movement of the vehicles • Uses the Minimum Description Length MDL for model selection • can be linked with compression • Applications: • detecting, • tracking and • compressing surveillance imagery

  17. HV Quadtree Parsing

  18. Quadtree Standard quadtree HV quatree The HV-quadtree gives an improved representation of an image yielding in some case up to 75% less regions. The fine resolution areas of the grid form the masks for ROI

  19. Phase Congruency • All image features have in common in the Fourier domain frequency components over a wide range - maximal in phase congruency • The angle at which this phase-congruency occurs is characteristic of the type of feature • For example: • +ve step = 0 • -ve step =  • +ve ridge = /2 • -ve ridge = 3/2 • A feature could be defined as the location at which there is a congruence of phase • It is invariant to contrast in a feature

  20. Phase Congruency The antennae together with their shadows particularly those in the distance are clearly extracted.

  21. Model Based Motion Tracking • Automatic recognition and tracking of vehicles in video sequences from fixed surveillance cameras • The technique: • fitting 2-D vehicle models to images • and then track the movement of the vehicles • Uses the Minimum Description Length MDL for model selection • can be linked with compression • Applications: • detecting, • tracking and • compressing surveillance imagery

  22. MPEG4 RAW Extracted Target Extracted + background 187 frames - small boat in the foreground 800:1

  23. Helicopter Tracking

  24. Performance Evaluation • Performance evaluation is important • Suitable metrication methods must be identified and implemented • Evaluation is a complex and many sided issue

  25. Target Detection Performance • The performance of the target detection process - Receiver Operating Characteristic (ROC) • % of true + detection of real targets/regions of interests • % of false - detection of target areas as background. • The targets/regions of interests are: • identified by the intelligence analyst and photographic interpreter • used as ground truth

  26. Performance Visualisation • There are many important dimensions of compression performance • Reduction of this complex space to single figures of merit destroys the necessary information • Understanding and assimilating this complex space: • is a significant problem for the human • a multi-dimensional graphical representation is necessary. • An interactive performance evaluation visualisation tool facilitates comparison of the performance of different compression approaches: • Target cueing in raw/decompressed images; • Information preservation; • Compression ratio; • Computation load; • Mean-square-error/Peak Signal-to-noise ratio; • Photographic interpreter and intelligence analyst’s assessment.

  27. Interactive Performance Visualisation Evaluate and assess: • Efficiency and effectiveness of different compression approaches • at different compression ratios in different circumstances • for different images and different types of images

  28. Master Battle Planner Situation Awareness, Mission Planning & battle damage assessment

  29. Evaluation Measures • How to measure the effectiveness of compression • How to measure the effectiveness of the compressed information in relation to the task: • Situation awareness • Military activities assessment • Monitoring • battle damage assessment