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DIGIT VIDEO SYSTEMS

DIGIT VIDEO SYSTEMS. A. ASTAPKOVICH. Lecture 0 COURSE REVIEW. State University of Aerospace Instrumentations , Saint-Petersburg, 2012. GOAL OF THE COURSE. Establishing the understanding of the basic principals of digit video systems : digit video processing cycle;

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DIGIT VIDEO SYSTEMS

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  1. DIGIT VIDEO SYSTEMS A. ASTAPKOVICH Lecture 0 COURSE REVIEW State University of Aerospace Instrumentations , Saint-Petersburg, 2012

  2. GOAL OF THE COURSE • Establishing the understanding of the basic principals of digit video systems : • digit video processing cycle; • structure of the modern digit video systems; • applications in multimedia, industrial, security, research ; • review of the modern research activities ;

  3. Course structure • PHYSICAL PRINCIPALS OF THE DIGIT FRAME FORMING AND HARDWARE/SOFTWARE REALIZATIONS • MODERN APPLICATIONS: MULTIMEDIA STANDARTS, • DIGIT VIDEO FOR INDUSTRY AND SPACE RESEARCH • DIGIT VIDEO PROCESSING BASICS • REVIEW OF MODERN RESEARCH ACTIVITY

  4. TOPIC 1.PHYSICAL AND REALIZATION BACKGROUD • Lecture 1. DIGIT CAMERA • § 1. Digit frame forming • § 2. Noise and distortionsin digit video • § 3. Low level processing • § 4. High level processing • § 5. Structure of the modern digit video camera • Lecture 2. DIGIT VIDEO • § 1. Digit video in multimedia • § 2. Multimedia standards review • § 3. Digit video in industry and space applications • § 4. Modern silicon solutions • § 5. System architecture and high level software

  5. DIGIT CAMERA STRUCTURE

  6. TOPIC 2. DIGIT VIDEO BASED SYSTEMS Lecture 3-4. IP-VIDEOPHONE AND SECURITY SYSTEMS §1. Packet communication nets §2. Family of IP-videophones CISCoand basics multimedia standards §3. Family of IP-videophones CISCoand basics multimedia standards §4. Audio codec and the quality of the videophones §5. Multichannel security video systems Cisco IP phone 7985 Videophone Digital Media System-on-Chip(DMSoC) TMS320DM365

  7. APPLICATION EXAMPLES AND SPACE STANDARTS Lecture 5-6. TECHNICAL VISION SYSTEMS §1. Digit video systems for industry applications §2. Review of the mars rover Spirit-Opportunity control system §3. Mars rover video system §4. Video in the mars rover control system loop §5. Special features of the digit channel §6. Space standards of ECSS Mars rover Spirit-Opportunity

  8. (a+b+c+d)/4 (a-b+c-d)/4 LL (a+b-c-d)/4 LH (a-b-c+d)/4 HL HH TOPIC 3. DIGIT IMAGE PROCESSING Lecture 7. DIGIT VIDEO COMPRESSING BASICS §1. Video stream parameters and and the image quality estimation §2. Video compression basic ideas §3. Wavelet compression and Haar basis §4. Review of the wavelet compression algorithms W0 HAAR wavelet 1 0 ≤ x ≤ ½ Ψ(x) =-1 ½ ≤ x ≤ 1 0 1 ≤ x; x ≤ 0 HAAR basis Ψ j i(x) = Ψ (2j x - i) i= 0..2 j -1 V1 V2

  9. IMAGE PROCESSING Lecture 8-9 IMAGE PROCESSING §1. Image quality estimation §2. Image filtering algorithms §3. Edge detectors §4. Moving object extractions Original image Enhanced contrast PSNR=25 dB JPEG compression PSNR=25 dB

  10. EDGE DETECTORS EDGE DETECTOR CANNY CLEAN AMAGE IMAGE EDGES NOISY IMAGE

  11. Encryption keys - K Marked information or STEGO - I” • Container - I • image • audio sample • text • code EMBEDDING ALGORITHM • Marking information - M • trade mark • copy number • other DIGIT VIDEOSTREAM PROTECTION Lecture 10. STEGANOGRAPHY § 1. Basic definitions and digit watermark classifications § 2. Digit watermark system structure § 3. System and algorithm requirements § 4. Attack review § 4. Application examples

  12. APPLICATION EXAMPLE Modified Kutter algorithm COMPRESSION JPEG, JPEG2000 LL LH HL HH WATERMARK EXSTRACTION • Q=55

  13. MODERN APPROACHES Lecture 11. ADAPTIVE ALGORITM PARADIGM § 1.Modern structural wavelet based norms SSIM and CW-SSIM § 2 Neuron net based algorithms § 3 Adaptive boosting learning ( ADA BOOST) Original image Enhanced contrast Distorted brightness Gauss noise MSE=0 SSIM=1 CW-SSIM=1 MSE=306 SSIM=0.928 CW-SSIM=0.938 MSE=309 SSIM=0.987 CW-SSIM=1 MSE=309 SSIM=0.576 CW-SSIM=0.814

  14. Noise proof edge detector on base of ANN NN Edge filter S1(0,0) S2(0,0) S1(0,1) S2(0,1) ................. 1 ……………………………….. S1(i,j) S2(i,j ) Snsen(i,j) 1 F(0,0) F(0,1) ……… F( i,j) w1 w2 wnsen+1 S * W = F min F(w) = (SW - F, SW – F)+  (W,W) w W = (ST S +  E) –1 ST F

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