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ETSI - TR 102 825-6 - DIGITAL VIDEO BROADCASTING (DVB); CONTENT PROTECTION AND COPY MANAGEMENT (DVB-CPCM); PART 6: CPCM SECURITY TEST VECTORS - V1.1.2 Organization: ETSI
Date: 2011-02-01
Description: The present document specifies the Security Test Vectors for the Digital Video Broadcasting (DVB) Content Protection and Copy Management (CPCM) system.
ITU-T H.763.2 - SCALABLE VECTOR GRAPHICS FOR IPTV SERVICES - STUDY GROUP 16 Organization: ITU-T
Date: 2017-03-01
Description: This Recommendation describes the functionalities of the scalable vector graphics (SVG) for IPTV services. SVG, as one of those standard multimedia application frameworks described in [ITU-T H.760], provides interoperable use of IPTV services.
CRC - YE21969 - ILLUSTRATOR FOUNDATIONS : THE ART OF VECTOR GRAPHICS, DESIGN AND ILLUSTRATION IN ILLUSTRATOR Organization: CRC
Date: 2012-12-13
Description: Want to hone your Illustrator skills so you can remain competitive in a diverse market while concentrating on your vector graphics projects? Then Illustrator Foundations is for you!
ITU-T J.343.4 - HYBRID-RR OBJECTIVE PERCEPTUAL VIDEO QUALITY MEASUREMENT FOR HDTV AND MULTIMEDIA IP-BASED VIDEO SERVICES IN THE PRESENCE OF A REDUCED REFERENCE SIGNAL AND NON-ENCRYPTED BITSTREAM DATA - STUDY GROUP 9 Organization: ITU-T
Date: 2014-11-01
Description: 1 This Recommendation includes an electronic attachment containing test vectors, including video sequences, bitstream files and predicted objective model scores.
ITU-T J.343.3 - HYBRID-RRE OBJECTIVE PERCEPTUAL VIDEO QUALITY MEASUREMENT FOR HDTV AND MULTIMEDIA IP-BASED VIDEO SERVICES IN THE PRESENCE OF A REDUCED REFERENCE SIGNAL AND ENCRYPTED BITSTREAM DATA - STUDY GROUP 9 Organization: ITU-T
Date: 2014-11-01
Description: 1 This Recommendation includes an electronic attachment containing test vectors, including video sequences, bitstream files and predicted objective model scores.
ITU-T J.343.5 - HYBRID-FRE OBJECTIVE PERCEPTUAL VIDEO QUALITY MEASUREMENT FOR HDTV AND MULTIMEDIA IP-BASED VIDEO SERVICES IN THE PRESENCE OF A FULL REFERENCE SIGNAL AND ENCRYPTED BITSTREAM DATA - STUDY GROUP 9 Organization: ITU-T
Date: 2014-11-01
Description: 1 This Recommendation includes an electronic attachment containing test vectors, including video sequences, bitstream files and predicted objective model scores.
ITU-T J.343.6 - HYBRID-FR OBJECTIVE PERCEPTUAL VIDEO QUALITY MEASUREMENT FOR HDTV AND MULTIMEDIA IP-BASED VIDEO SERVICES IN THE PRESENCE OF A FULL REFERENCE SIGNAL AND NON-ENCRYPTED BITSTREAM DATA - STUDY GROUP 9 Organization: ITU-T
Date: 2014-11-01
Description: The following source reference channel (SRC) conditions were included in the validation test: 1080i 60 Hz (29.97 fps); 1080p (25 fps); 1080i 50 Hz (25 fps); 1 080p (29.97 fps); S RC duration: HD: 10s, VGA/WVGA: 10 s or 15 s (rebuffering); VGA at 25 and 30 fps; WVGA at 25 and 30 fp 1 This Recommendation includes an electronic attachment containing test vectors, including video sequences, bitstream files and predicted objective model scores.
ITU-T J.343.2 - HYBRID-NR OBJECTIVE PERCEPTUAL VIDEO QUALITY MEASUREMENT FOR HDTV AND MULTIMEDIA IP-BASED VIDEO SERVICES IN THE PRESENCE OF NON-ENCRYPTED BITSTREAM DATA - STUDY GROUP 9 Organization: ITU-T
Date: 2014-11-01
Description: The following source reference channel (SRC) conditions were included in the validation test: 1080i 60 Hz (29.97 fps); 1080p (25 fps); 1080i 50 Hz (25 fps); 1080p (29.97 fps); SRC duration: HD: 10 s, VGA/WVGA: 10 s or 15 s (rebuffering); VGA at 25 and 30 fps; WVGA at 25 and 30 fp 1 This Recommendation includes an electronic attachment containing test vectors, including video sequences, bitstream files and predicted objective model scores.
ITU-T J.343.1 - HYBRID-NRE OBJECTIVE PERCEPTUAL VIDEO QUALITY MEASUREMENT FOR HDTV AND MULTIMEDIA IP-BASED VIDEO SERVICES IN THE PRESENCE OF ENCRYPTED BITSTREAM DATA - STUDY GROUP 9 Organization: ITU-T
Date: 2014-11-01
Description: 1 This Recommendation includes an electronic attachment containing test vectors, including video sequences, bitstream files and predicted objective model scores.
ITU-T J.340 - REFERENCE ALGORITHM FOR COMPUTING PEAK SIGNAL TO NOISE RATIO OF A PROCESSED VIDEO SEQUENCE WITH COMPENSATION FOR CONSTANT SPATIAL SHIFTS, CONSTANT TEMPORAL SHIFT, AND CONSTANT LUMINANCE GAIN AND OFFSET - STUDY GROUP 9 Organization: ITU-T
Date: 2010-06-01
Description: Since the calculation of PSNR is highly dependent upon proper estimation of spatial alignment, temporal alignment, gain, and level offset between the processed video sequence and the original video sequence, the method of measurement for PSNR should ideally include a method for performing these calibration procedures.
ETSI - TS 126 234 - UNIVERSAL MOBILE TELECOMMUNICATIONS SYSTEM (UMTS); LTE; TRANSPARENT END-TO-END PACKET-SWITCHED STREAMING SERVICE (PSS); PROTOCOLS AND CODECS - V14.0.0; 3GPP TS 26.234 VERSION 14.0.0 RELEASE 14 Organization: ETSI
Date: 2017-04-01
Description: Codecs for speech, natural and synthetic audio, video, still images, bitmap graphics, vector graphics, timed text and text are specified.
NEN-ISO/IEC 23001-7 - INFORMATION TECHNOLOGY - MPEG SYSTEMS TECHNOLOGIES - PART 7: COMMON ENCRYPTION IN ISO BASE MEDIA FILE FORMAT FILES Organization: NEN
Date: 2016-02-01
Description: Subsample encryption is specified for NAL structured video, such as AVC and HEVC, to enable normal processing and editing of video elementary streams prior to decryption.
ISO/IEC 23001-7 - INFORMATION TECHNOLOGY - MPEG SYSTEMS TECHNOLOGIES - PART 7: COMMON ENCRYPTION IN ISO BASE MEDIA FILE FORMAT FILES - THIRD EDITION Organization: ISO
Date: 2016-02-15
Description: Subsample encryption is specified for NAL structured video, such as AVC and HEVC, to enable normal processing and editing of video elementary streams prior to decryption.
CSA ISO/IEC 23001-7 - INFORMATION TECHNOLOGY - MPEG SYSTEMS TECHNOLOGIES - PART 7: COMMON ENCRYPTION IN ISO BASE MEDIA FILE FORMAT FILES Organization: CSA
Date: 2018-01-01
Description: Subsample encryption is specified for NAL structured video, such as AVC and HEVC, to enable normal processing and editing of video elementary streams prior to decryption.
CRC - E9774 - COLOR IMAGE PROCESSING : METHODS AND APPLICATIONS Organization: CRC
Date: 2006-10-20
Description: The remaining chapters detail the latest techniques and approaches to contemporary and traditional color image processing and analysis for a broad spectrum of sophisticated applications, including: Vector and semantic processing Secure imaging Object recognition and feature detection Facial and retinal image analysis Digital camera image processing Spectral and superresolution imaging Image and video colorization Virtual restoration of artwork Video shot segmentation and surveillance Color Image Processing: Methods and Applications is a versatile resource that can be used as a graduate textbook or as stand-alone reference for the design and the implementation of various image and video processing tasks for cutting-edge applications.
CRC - INTRODUCTION TO MACHINE L - INTRODUCTION TO MACHINE LEARNING WITH APPLICATIONS IN INFORMATION SECURITY Organization: CRC
Date: 2017-09-07
Description: The book covers core machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. It also includes coverage of Nearest Neighbors, Neural Networks, Boosting and AdaBoost, Random Forests, Linear Discriminant Analysis, Vector Quantization, Naive Bayes, Regression Analysis, Conditional Random Fields, and Data Analysis.
DS/ISO/IEC 14496-20/AMD 1 - INFORMATION TECHNOLOGY - CODING OF AUDIO-VISUAL OBJECTS - PART 20: LIGHTWEIGHT APPLICATION SCENE REPRESENTATION (LASER) AND SIMPLE AGGREGATION FORMAT (SAF) Organization: DS
Date: 2008-04-03
Description: A rich media service is a dynamic, interactive collection of multimedia data such as audio, video, graphics, and text. Services range from movies enriched with vector graphic overlays and interactivity (possibly enhanced with closed captions) to complex multi-step services with fluid interaction and different media types at each step.
PACKT - OPENCV: COMPUTER VISION P - OPENCV: COMPUTER VISION PROJECTS WITH PYTHON Organization: PACKT
Date: 2016-10-24
Description: What You Will Learn • Install OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect - all on Windows, Mac or Ubuntu • Apply ""curves"" and other color transformations to simulate the look of old photos, movies, or video games • Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image • Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor • Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques • Detect and recognize street signs using a cascade classifier and support vector machines (SVMs) • Identify emotional expressions in human faces using convolutional neural networks (CNNs) and SVMs • Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features In Detail OpenCV is a state-of-art computer vision library that allows a great variety of image and video processing operations.
CRC - KE11239 - 3D GRAPHICS FOR GAME PROGRAMMING Organization: CRC
Date: 2011-02-17
Description: Assuming a minimal prerequisite understanding of vectors and matrices, it also provides sufficient mathematical background for game developers to combine their previous experience in graphics API and shader programming with the background theory of computer graphics.
PACKT - MASTERING OPENCV 3 - MASTERING OPENCV 3 - SECOND EDITION Organization: PACKT
Date: 2017-04-28
Description: What You Will Learn • Execute basic image processing operations and cartoonify an image • Build an OpenCV project natively with Raspberry Pi and cross-compile it for Raspberry Pi.text • Extend the natural feature tracking algorithm to support the tracking of multiple image targets on a video • Use OpenCV 3’s new 3D visualization framework to illustrate the 3D scene geometry • Create an application for Automatic Number Plate Recognition (ANPR) using a support vector machine and Artificial Neural Networks • Train and predict pattern-recognition algorithms to decide whether an image is a number plate • Use POSIT for the six degrees of freedom head pose • Train a face recognition database using deep learning and recognize faces from that database In Detail As we become more capable of handling data in every kind, we are becoming more reliant on visual input and what we can do with those self-driving cars, face recognition, and even augmented reality applications and games.

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