DOCSLIB.ORG

Video Enhancement Techniques Employed Over the Decade for Reliable Communication

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Video Enhancement Techniques Employed Over the Decade for Reliable Communication International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-11, September 2019 Video Enhancement Techniques Employed Over the Decade for Reliable Communication Padma Reddy A.M, Udaya Rani.V, S.Raj Barath Abstract - Traditional networks are designed to Recently, several resolution methods have been meet the needs of streaming video on the Internet. launched and are very useful for collecting information. The Relatively high standards of broadband connections purpose of the study is to analyze high definition videos and and video broadband services make the best effort for vague videos. This study includes an optimization method certain specific video information and transmission for creating online video source services. The program methods to appear at any time to reduce network constraints. Video quality is directly related to the focuses on application optimization, facilitating the basic ability of the phone to send and receive data recording of video clips between endpoints. The purpose of from time to time for playback. Record videos from each system is to overcome the overall quality of the user your mobile phone are facing various challenges that by using the default values. Although there are some have been overcome by a variety of methods. In an interesting challenges for data transfer law, the law on extremely robust and unpredictable environment, networks and migration, this study is based on network- transversal hardware provides adequate video. based networks, not networks. Conventional networks are designed to meet the needs of streaming video over the Internet. Relatively high standards of broadband connections and video II. NETWORK VIDEO DELIVERY broadband services make the best effort for certain Video sources are usually divided into two specific video information and transmission methods categories: to appear at any time to reduce network constraints. Video quality is directly related to the basic ability of A) Real time video the phone to send and receive data from time to time for playback. Record videos from your mobile phone In real-time, the video provides databases and, over are facing various challenges that have been overcome by a variety of methods. In an extremely time, sets more stringent requirements because there is no robust and unpredictable environment, transversal buffer on the server. Frames are created and transmitted in hardware provides adequate video. real time, and clients receive and execute real-time frames. All Keywords- c r o s s -layer m e c h a n i s m s ; v i d e o delivery; real time video processing’s need to be processed on time. video streaming; These time requirements generally prohibit the processing of high-quality, high-performance and high-performance I. INTRODUCTION processors. The role of video streaming has grown B) Non Real time video significantly over the last decade. Consumers accept convenience, flexibility and variety through online videos They are usually pre-registered and available for but opt for new tax revenue for service providers. Real-time permanent applications. The availability of files in unusual monitoring is a refreshing video for low-quality videos. The situations allows you to recycle the video. Access to all videos key to the popularity of the video on the web is direct, high also features functions such as a digital video recorder (DVR) quality, undefined and continuous playback. An important (such as a pause, quick and fast reversal), as data can easily be step in measuring the user experience is the following: (for retrieved from different offsets. example, interruptions, disturbances or dots in the video) There are also real-time video clocks in which live the original reproduction time (for example, how long does channels can be delivered with compatible and compatible it take to view the videos and press the play button on the copies. This hybrid method uses the real-time real-time screen). Reduce standby time and reduce distortion and animation benefits in real-time. The main difference between termination, depending on the system's ability to transmit time and shutdown time is actually how video is being video data. The higher the power, the original video formatted. For near-real-time video, data must be stored in a recorder speeds up, but requires a minimum speed that is format that does not require full metadata at the beginning of higher than the image speed to prevent the image [1]. the file. HTTP Live Streaming (HLS) is an example of near- real-time delivery, where the video is recorded in objects and stored as a custom file format.The digital video has become an integral part of everyday life. The primary purpose of video Revised Manuscript Received on September 06, 2019 enhancement is to convey detailed hidden information in the Padma Reddy A.M, Research Scholar, School of Computing and video. The issue of video growth is to retain high quality Information Technology, Reva University, Kattigenahalli,Yelahanka , Bengaluru -64 video input and high quality video output for certain Dr.Udaya Rani.V, Senior Associate Professor, School of Computing applications. and Information Technology, Reva University, Kattigenahalli,Yelahanka , Bengaluru -64 Dr.S.Raj Barath, Associate Professor, Department of Electronics and Communication Engineering, Sai Vidya Institute of Technology, Rajanukunte, Bengaluru -64 Published By: Retrieval Number: K14000981119/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijitee.K1400.0981119 3987 & Sciences Publication Video Enhancement Techniques Employed Over the Decade for Reliable Communication There are many applications where videos, such as avid downloads reduce the impact of downloading while surveillance, general characteristics, trafficking, criminal waiting for large files; Reproduction time may be very high. justice, civil or military video processing, etc. are purchased, By entering a gradual download, you can control playback processed and used. time directly from live downloads. Fixing videos with low-quality video is a For gradual download, network and playback are challenging issue for the following reasons: (i) Due to low separated for preview to start downloading all files. This contrast; we cannot clearly reduce the moving objects from feature allows the system to effectively control the the dark environment. (ii) Signal strength is usually very download speed from both the client and the server. While low due to high ISO (ISO is the code indicating sensitivity expressing direct downloads and gradual downloads is often of the camera sensor). Using high-quality ISO numbers can used to describe transportation methods and game counters, create digital noise. Low ISO numbers are less sensitive to it is important to separate these activities [3]. For this light. The quality of the video recorder used is poor and the reason, we believe that f ast and fast delivery can be operator experience is insufficient. To overcome this demonstrated. Most modern media players have progressive limitation, the sample allows you to create different video display capabilities. This includes the initial copy before all effects, such as direct and transparent filtering, which works data is recovered, and the data provided eliminates the directly in the noise without the need for proper remote buffer requirements. reconstruction or repair. The test is based on a variety of handy applications III. RESOLUTION ENHANCEMENT AND CROSS- ranging from basic image processing, such as noise LAYER OPTIMISATION PRINCIPLES reduction, blurring and high resolution to advanced video A. General Approach effects such as action pictures, street-side elimination for real-world objects, and releases the shutter on your Layered architecture, such as the seven-tier OSI computer. Removing the video received less attention than model [4], divides all network operators into points and removing photos from the literature. In principle, the defines the level of service level provided by each level. generation of video noise should be reduced, but images The very limited network architecture prohibits direct such as strong transient redundancy make it easier to load communication between independent levels and on the road to eliminate noise. However, this form of communication between adjacent levels is limited to additional redundancy is a challenge. On the one hand, the procedural calls and responses. Alternatively, protocols can amount of data is much larger than the recorded image and be designed to violate the reference architecture, for requires an efficient way of navigating the data. example, by allowing direct communication between the On the other hand, the result of the noise reduction protocols in the base law or the law variable. Violation of algorithm should be temporary, according to the unknown this architectural level is a multilateral optimization method sequence of the first series, which are fundamental quality that refers to the Code of Conduct by using protocol requirements. Immediately the idea is to hide animation, changes to achieve better results. In multidisciplinary approaches, instead of judging the law as completely analyze and observe methods that use one or more numbers independent of practical organisms, information can be in your image. Unlike disturbing photos, sequential video divided between points in both directions: the lower and the behaviors are recorded in time and space, which makes lowest. This information exchange can be used to fully
Load more

Recommended publications
  • Video Denoising, Deblocking and Enhancement Through Separable
    1 Video Denoising, Deblocking and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms Matteo Maggioni, Giacomo Boracchi, Alessandro Foi, Karen Egiazarian Abstract—We propose a powerful video filtering algorithm that the nonlocal similarity between patches at different locations exploits temporal and spatial redundancy characterizing natural within the data [5], [6]. Algorithms based on this approach video sequences. The algorithm implements the paradigm of have been proposed for various signal-processing problems, nonlocal grouping and collaborative filtering, where a higher- dimensional transform-domain representation of the observations and mainly for image denoising [4], [6], [7], [8], [9], [10], [11], is leveraged to enforce sparsity and thus regularize the data: [12], [13], [14], [15]. Specifically, in [7] has been introduced 3-D spatiotemporal volumes are constructed by tracking blocks an adaptive pointwise image filtering strategy, called non- along trajectories defined by the motion vectors. Mutually similar local means, where the estimate of each pixel xi is obtained volumes are then grouped together by stacking them along an as a weighted average of, in principle, all the pixels x of additional fourth dimension, thus producing a 4-D structure, j termed group, where different types of data correlation exist the noisy image, using a family of weights proportional to along the different dimensions: local correlation along the two the similarity between two neighborhoods centered at xi and dimensions of the blocks, temporal correlation along the motion xj. So far, the most effective image-denoising algorithm is trajectories, and nonlocal spatial correlation (i.e. self-similarity) BM3D [10], [6], which relies on the so-called grouping and along the fourth dimension of the group.
    [Show full text]
  • ICIP 2007 Session Index
    ICIP 2007 Session Index Monday Morning MA-L1: Video Coding for Next Generation Displays MA-L2: Image And Video Segmentation I MA-L3: Video Coding I MA-L4: Image and Video Restoration MA-L5: Biometrics I MA-L6: Image and Video Storage and Retrieval I MA-P1: Stereoscopic and 3D Processing I: Coding and Processing MA-P2: Active-Contour, Level-Set, and Cluster-Based Segmentation Methods MA-P3: Image and Video Denoising MA-P4: Biometrics II: Human Activity, Gait, Gaze Analysis MA-P5: Security I: Authentication and Steganography MA-P6: Image and Video Multiresolution Processing MA-P7: Motion Detection and Estimation I MA-P8: Image and Video Enhancement Monday Afternoon MP-L1: Distributed Source Coding I: Low Complexity Video Coding MP-L2: Image And Video Segmentation II: Texture Segmentation MP-L3: Interpolation and Superresolution I MP-L4: Image and Video Modeling I MP-L5: Security II MP-L6: Image Scanning, Display, Printing, Color and Multispectral Processing I MP-P1: Image and Video Storage and Retrieval II MP-P2: Morphological, Level-Set, and Edge or Color Image/Video Segmentation MP-P3: Scalable Video Coding MP-P4: Image Coding I MP-P5: Biometrics III: Fingerprints, Iris, Palmprints MP-P6: Biomedical Imaging I MP-P7: Motion Detection and Estimation II MP-P8: Stereoscopic and 3D Processing II: 3D Modeling & Synthesis Tuesday Morning TA-L1: Distributed Source Coding II: Distributed Image and Video Coding and Their Applications TA-L2: Image and Video Segmentation III: Edge or Color Segmentation TA-L3: Stereoscopic and 3D Processing III TA-L4:
    [Show full text]
  • Universidad Politécnica De Valencia
    Universidad Polit´ecnica de Valencia Departamento de Comunicaciones Tesis Doctoral T´ecnicasdean´alisis de secuencias de v´ıdeo. Aplicaci´on a la restauraci´on de pel´ıculas antiguas Presentada por: Valery Naranjo Ornedo Dirigida por: Dr. Antonio Albiol Colomer Valencia, 2002. ALuisyaFran “La mera formulaci´on de un problema suele ser m´as esencial que su soluci´on, la cual puede ser una simple cuesti´on de habilidad matem´atica o experimental. Plantear nuevas preguntas, nuevas posibilidades, contemplar viejos problemas des- de un nuevoangulo, ´ exige imaginaci´on creativa y marca adelantos reales en la ciencia.” Albert Einstein Agradecimientos Es muy dif´ıcil mostrar mi agradecimiento, con unas simples palabras, a todas aquellas personas que han hecho que haya llegado hasta aqu´ı, a´un as´ı, no quer´ıa dejar pasar la opor- tunidad de intentarlo. En primer lugar quiero mostrar mi agradecimiento a Antonio Albiol, que ha sido no s´olo mi director de tesis, sino tambi´en mi amigo, y mi maestro en todo lo que s´e de procesado de se˜nal. A mi familia y amigos por estar ah´ı siempre que los necesito, sin esperar nada a cambio, y sobre todo, por tener fe en m´ı. A Luis, mi marido, que siempre me apoya y me ayuda en todo, y hace que todos los esfuerzos tengan sentido. Amiscompa˜neros del Departamento de Comunicaciones que me han echado una mano en esta empresa: a Jos´e Manuel, por su paciencia, sus consejos y su ayuda desinteresada e inestimable; a Luis Vergara por tantas dudas de tratamiento de se˜nal resueltas, a Mar´ıa y Angel´ por sus observaciones y revisiones, a Paco y Pablo por sus consejos ling¨u´ısticos, y a Juan Carlos por sus consejos burocr´aticos.
    [Show full text]
  • Baselight Software Flexibility, Power and Throughput
    Baselight software Flexibility, power and throughput The world’s most powerful colour grading and finishing system for film, TV, commercials and broadcast. Power and capacity Start grading sooner Baselight is available on a range of hardware platforms Baselight is straightforward to use. The UI and control to deliver the power required for intensive grading. The panels are logically laid out so you become productive unique Baselight architecture can deliver real-time 4K quickly. FilmLight also provides bespoke training options, performance and up to a massive 204TB of storage, while together with highly qualified and responsive 24-hour its comprehensive GPU renderer handles all operations product support via web, email and phone. natively for the most accurate and efficient throughput. This power enables Baselight to perform complex, multi- Creative options layered grading operations in real time while carrying With Baselight, workspaces can be defined and saved for out intensive tasks such as ingest and rendering in the different tasks, such as grading, conforming or just personal background. preference. Even the control surfaces can be customised to your preferred way of working using our Chalk application. Extensive control Baselight is a full-featured grading suite—it can handle high- Blackboard 2 takes colour grading to the next level with end commercials and beauty spots as well as problem context-sensitive controls, high-resolution displays and footage and stereo productions with ease. But as business programmable, adaptive keys. There’s also our Slate needs change and technology advances, the system can panel, which uses the same technological advances as be upgraded and extended.
    [Show full text]
  • IDEAL: Image Denoising Accelerator
    IDEAL: Image DEnoising AcceLerator Mostafa Mahmoud Bojian Zheng Alberto Delmás Lascorz University of Toronto University of Toronto University of Toronto [email protected] [email protected] [email protected] Felix Heide Jonathan Assouline Paul Boucher Stanford University/Algolux Algolux Algolux [email protected] [email protected] [email protected] Emmanuel Onzon Andreas Moshovos Algolux University of Toronto [email protected] [email protected] ABSTRACT CCS CONCEPTS Computational imaging pipelines (CIPs) convert the raw output • Computer systems organization → Special purpose systems; of imaging sensors into the high-quality images that are used for Neural networks; Real-time system architecture; Single instruction, further processing. This work studies how Block-Matching and multiple data; 3D filtering (BM3D), a state-of-the-art denoising algorithm canbe implemented to meet the demands of user-interactive (UI) appli- KEYWORDS cations. Denoising is the most computationally demanding stage Computational imaging, image denoising, accelerator, neural net- of a CIP taking more than 95% of time on a highly-optimized soft- works ware implementation [29]. We analyze the performance and energy ACM Reference format: consumption of optimized software implementations on three com- Mostafa Mahmoud, Bojian Zheng, Alberto Delmás Lascorz, Felix Heide, modity platforms and find that their performance is inadequate. Jonathan Assouline, Paul Boucher, Emmanuel Onzon, and Andreas Moshovos. Accordingly, we consider two alternatives: a dedicated acceler- 2017. IDEAL: Image DEnoising AcceLerator. In Proceedings of MICRO-50, ator, and running recently proposed Neural Network (NN) based Cambridge, MA, USA, October 14–18, 2017, 14 pages. approximations of BM3D [9, 27] on an NN accelerator.
    [Show full text]
  • An Estimation-Theoretic Approach to Video Denoiseing
    AN ESTIMATION-THEORETIC APPROACH TO VIDEO DENOISEING Jingning Han, Timothy Kopp, and Yaowu Xu Google Inc. 1600 Amphitheatre Parkway, Mountain View, CA 94043 Emails: {jingning,tkopp,yaowu}@google.com ABSTRACT compression codec and pre/post processing to be able to han- dle a 720p sequence at 30 fps on a single thread general pur- A novel denoising scheme is proposed to fully exploit the pose processor. A fairly common approach for real-time com- spatio-temporal correlations of the video signal for efficient munication is to integrate the denoiser into the video codec, enhancement. Unlike conventional pixel domain approaches which allows the block motion information to be used by both that directly connect motion compensated reference pixels denoiser and compressor, hence the system only requires a and spatially neighboring pixels to build statistical models for single motion estimation process per block, thereby substan- noise filtering, this work first removes spatial correlations by tially reducing the overall computational load [12]. The un- applying transformations to both pixel blocks and performs derlying assumption is that the video signal dominates motion estimation in the frequency domain. It is premised on the search even in the presence of acquisition noise and hence realization that the precise nature of temporal dependencies, the resulting motion trajectories largely reflect the true motion which is entirely masked in the pixel domain by the statis- flow, which generally holds true for typical video conferenc- tics of the dominant low frequency components, emerges ing scenarios particularly at 480p and above frame size. Such after signal decomposition and varies considerably across practical constraints rise the major challenge that we will ad- the spectrum.
    [Show full text]
  • Telecine STE-B1
    Telecine STE-B1 Operating Instructions Published by BTS Media Solutions GmbH Brunnenweg 9 D-64331 Weiterstadt, Germany P.O. Box 1165 Tel: +49 (0) 6155-870-0 Fax: +49 (0) 6155-870-300 Web Sites Internet: www.thomsonbroadcast.com www.imagingsystems.de Intranet: www.weiterstadt.thmulti.com Trademarks All product names mentioned in this manual are the trademarks of their respective owners. Copyrights Information in this document is subject to change without notice. This document and any updates and/or supplemental information, including any copies thereof, cannot be reproduced, neither communicated to a third party, without written authorization from THOMSON multimedia Broadcast Solutions. Please notify THOMSON multimedia Broadcast Solutions of any errors in this document. We also would appreciate any comments you have to improve this manual. E BTS Media Solutions GmbH 2002. All rights reserved. STE-B1 Contents CONTENTS 1. General 1.1 About the Manual. 1-1 1.2 Software State. 1-1 2. Control and Display Elements 2.1 Filmdeck. 2-1 2.2 Control Panels. 2-5 2.2.1 Local Control Panel FH 4500. 2-5 2.2.2 Graphical Control Panel GCP. 2-8 3. Operational Preparations 3.1 Hardware Conditions. 3-1 3.2 Power-Up. 3-1 3.3 Basic Adjustments of the Telecine. 3-2 3.3.1 Configuration of the Control Panel. 3-2 3.3.2 Select TV Standard. 3-3 3.4 User-Specific Configuration of the Telecine. 3-5 3.4.1 Change Optical Block. 3-5 3.4.2 Change Film Gate. 3-7 3.4.3 Using Effect Filters.
    [Show full text]
  • (12) United States Patent (10) Patent No.: US 7,119,837 B2 Soupliotis Et Al
    US007119837B2 (12) United States Patent (10) Patent No.: US 7,119,837 B2 Soupliotis et al. (45) Date of Patent: Oct. 10, 2006 (54) VIDEO PROCESSING SYSTEM AND FOREIGN PATENT DOCUMENTS METHOD FOR AUTOMATIC ENHANCEMENT OF DIGITAL VIDEO EP O 571 121 A2 11, 1993 WO WO O2/39737 A1 5, 2002 (75) Inventors: Andreas Soupliotis, Bothell, WA (US); OTHER PUBLICATIONS Padmanabhan Anandan, Sammamish, Altunbasak, Y. aand A. M. Tekalp. Occlusion-Adaptive, Content WA (US) Based Mesh Design and Forward tracking. IEEE Transations on Image Processing, vol. 6, No. 9, Sep. 1997. (73) Assignee: Microsoft Corporation, Redmond, WA Bab-Hadiashar, A. and D. Suter. Robust optic flow estimation using (US) least median of squares. ICIP'96 IEEE International Conference on Image Processing, Sep. 16-19, Switzerland. (*) Notice: Subject to any disclaimer, the term of this Ben-Ezra, M. S. Peleg, and M. Werman. Real-Time Motion Analy patent is extended or adjusted under 35 sis with Linear-Programming. Institute of Computer Science. The U.S.C. 154(b) by 840 days. Hebrew University of Jerusalem. 91904 Jerusalem, Israel. Borman, S. and R.L. Stevenson. Simultaneous multi-frame MAP (21) Appl. No.: 10/186,562 Super-resolution video enhancement using spatio-temporal priors. Dept. of Electrical Engineering, University of Notre Dame. Notre (22) Filed: Jun. 28, 2002 Dame, IN. USA. (65) Prior Publication Data (Continued) Primary Examiner Tuan Ho US 2004/OOO 1705 A1 Jan. 1, 2004 (74) Attorney, Agent, or Firm—Lyon & Harr, L.L.P.; Craig (51) Int. Cl. S. Fischer H04N 5/228 (2006.01) (57) ABSTRACT (52) U.S.
    [Show full text]
  • An Innovative Approach for Denoising of the Digital Video Stream
    Journal of University of Shanghai for Science and Technology ISSN: 1007-6735 AN INNOVATIVE APPROACH FOR DENOISING OF THE DIGITAL VIDEO STREAM Anand B. Deshmukh1 and Dr. Sanjay V. Dudul2 1Assistant Professor Department of Information Technology Sipna College of Engineering and Technology, Amravati (MH), India. 2Prof. and Head, Department of Applied Electronics, Sant Gadge Baba Amravati University, Amravati (MH), India. [email protected] [email protected] Abstract: Everyday tones of video signals are generated, transmitted and analyzed. The video contents are created for educational purpose, entertainment purpose, surveillance purpose, medical imaging purpose, weather forecasting, satellite imaging and many other significant places. During the different phases of video content preparation, transmission and analysis some unwanted signal get interfered with the true contents. Particularly, the medical imaging signals, since they are weak signals, are more prone to the unwanted interferences. Such unwanted interference of the noise signals makes it difficult to analyze the critical information in the video contents and hence, need of denoising process arises. A decent video denoising framework assures visual improvement in the video signals or it serves as the significant pre-processing step in the video processing steps like compression and analysis. Through this paper, we are about to disclose an efficient video denoising framework which takes the noisy video signal in the form of frames per second, and performs the video denoising using shot detection, compensation, intensity calculations and motion estimation process. Keywords: Video denoising, PSNR, SSIM, MSSIM 1. INTRODUCTION It was revealed in the press event of the YouTube that, different platform users are uploading 500 hours of video contents every minute [1], this takes us to 30,000 hours of video contents every hour and 7,20,000 hours of data per day.
    [Show full text]
  • Noise Estimation for Video Processing Based on Spatial-Temporal
    IEEE SIGNAL PROCESSING LETTERS, VOL. 13, NO. 6, JUNE 2006 337 Noise Estimation for Video Processing Based on Spatio–Temporal Gradients Vladimir Zlokolica, Student Member, IEEE, Aleksandra Pizuricaˇ , Member, IEEE, and Wilfried Philips, Member, IEEE Abstract—We propose an efficient and accurate wavelet-based sequence. Our initial noise estimate is proportional to the value noise estimation method for white Gaussian noise in video se- at which the spatial or temporal gradient-histogram reaches its quences. The proposed method analyzes the distribution of spatial maximum. The decision of whether to use the spatial or tem- and temporal gradients in the video sequence in order to estimate poral gradient histogram is based on the deviation of the gra- the noise variance. The estimate is derived from the most frequent gradient in the two distributions and is compensated for the dient-histogram from the Rayleigh distribution, and so is the errors due to the spatio–temporal image sequence content, by correction of the initial estimate. The implementation of these a novel correction function. The spatial and temporal gradients ideas is an efficient scheme suitable for real-time applications. are determined from the finest scale of the spatial and temporal The experimental results show that the proposed method is more wavelet transform, respectively. The main application of the noise accurate than the state-of-the-art techniques and less sensitive estimation algorithm is in wavelet-based video processing. The to varying noise levels and the presence of spatio–temporal se- results show that the proposed method is more accurate than other state-of-the-art noise estimation techniques and less sensitive to quence content.
    [Show full text]
  • Image Transformation and Video Compression Techniques
    Conference Proceeding Issue Published in International Journal of Trend in Research and Development (IJTRD), ISSN: 2394-9333, www.ijtrd.com Image Transformation and Video Compression Techniques 1D. Viji and 2S. Kothbul Zeenath Banu, 1,2Assistant Professor, Department of Computer Application, Krishnasamy College of Science, Arts and Management for Women, Cuddalore, India Abstract: Image and Video Processing are fortune topic of the Group (JPEG) is an excellent way to store 24-bit photographic field of research and development. Image processing is any images, such as those used for imaging and multimedia form of signal processing for which the input is an image, such applications. JPEG 24- bit (16 million color) images are as all types of image files, photographs or frames of video, the superior in appearance to 8-bit (256 color) images on a Video output of image processing can be either an image or a set of Graphics Array (VGA) display and are at their most characteristics related to the image. Image transformation spectacular. techniques used to transmit an image. Video processing is a particular case of signal processing, where the input and output TIFF: The TIFF (Tagged Image File Format) is a flexible signals are video files or video streams. Framing and Video format that can be lossless or lossy compression [8]. It Compressing techniques are used in Television, VCRs, DVDs, normally saves 8 bits or 16 bits per color (red, green, blue) for video codec, video players and other devices. In This paper, we 24-bit and 48-bit totals, respectively. The details of the image present Image transformation and Video Compression storage algorithm are included as part of the file.
    [Show full text]
  • Digitale Video Titelpagina Ontbreekt Met Dank Aan Wim Tilkin (
    Digitale video titelpagina ontbreekt Met dank aan Wim Tilkin (www.creativeskills.be/user/wimpos) Meer informatie en downloadbestanden bij dit boek vindt u op www.computerkit.be en www.myhacks.be Copyright © 2009 Easy Computing 1e druk 2009 Uitgever Easy Computing Publishing N.V. Horzelstraat 100 1180 Brussel Easy Computing B.V. Jansweg 40 2011 KN Haarlem E-mail [email protected] Web www.easycomputing.com Auteur Kris Merckx Eindredactie Joost Devriesere | zin redactie&copy Vormgeving Phaedra creative communications Cover Sari Vandermeeren ISBN 978-90-456-4777-7 NUR 985 Wettelijk Depot D/2009/6786/53 Alle rechten voorbehouden. Zonder voorafgaande schriftelijke toestemming van Easy Computing mag niets van deze uitgave worden verveelvoudigd, be- werkt en/of openbaar gemaakt door middel van druk, fotokopie, microfilm, magnetische media of op welke andere wijze ook. Belangrijke opmerking Wanneer in dit boek methodes en programma’s worden vermeld, gebeurt dit zonder inachtneming van patenten, aangezien ze voor amateur- en studiedoeleinden dienen. Alle informatie in dit boek werd door de auteur met de grootste zorgvuldigheid verzameld respectievelijk samengesteld. Toch zijn fouten niet helemaal uit te sluiten. Easy Computing neemt daarom noch garantie, noch juridische verantwoordelijkheid of enige andere vorm van aansprakelijkheid op zich voor de gevolgen van onjuiste of onvolledige informatie. Wanneer u eventuele fouten tegenkomt, zijn de auteur en de uitgever dankbaar wanneer u deze aan hen doorgeeft. Wij wijzen er verder op dat de in
    [Show full text]