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

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Presentation on theme: "DIGIT VIDEO SYSTEMS A. ASTAPKOVICH State University of Aerospace Instrumentations, Saint-Petersburg, 2012 Lecture 0 COURSE REVIEW."— Presentation transcript:

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

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

7 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 APPLICATION EXAMPLES AND SPACE STANDARTS

8 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 HAAR wavelet 1 0 ≤ x ≤ ½ Ψ(x) = -1 ½ ≤ x ≤ 1 Ψ(x) = -1 ½ ≤ x ≤ ≤ x; x ≤ ≤ x; x ≤ 0 HAAR basis Ψ j i(x) = Ψ (2 j x - i) i= 0..2 j -1 i= 0..2 j -1 (a+b+c+d)/4(a-b+c-d)/4 (a+b-c-d)/4(a-b-c+d)/4 LLLH HLHH W0W0 V1V1 V2V2

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

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

11 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 Encryption keys - K Container - I image audio sample text code Marking information - M trade mark copy number other EMBEDDING ALGORITHM Marked information or STEGO - I”

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

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 MSE=0 SSIM=1 CW-SSIM=1 MSE=306 SSIM=0.928 CW-SSIM=0.938 Enhanced contrastDistorted brightness MSE=309 SSIM=0.987 CW-SSIM=1 Gauss noise MSE=309 SSIM=0.576 CW-SSIM=0.814

14 Noise proof edge detector on base of ANN S 1 (0,0) S 2 (0,0) S 1 (0,1) S 2 (0,1) ……………………………….. S 1 ( i,j ) S 2 (i,j ) S nsen ( i,j ) 1 w 1 w 2 w nsen+1 F(0,0) F(0,1) ……… F( i,j) W = (S T S +  E) –1 S T F min F(w) = (SW - F, SW – F)+  (W,W) w S * W = F NN Edge filter


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