Video processing involves manipulating and analyzing digital video sequences. Common techniques include trimming, resizing, adjusting brightness/contrast, and analyzing using machine learning. Key concepts in video include compression, frames, frame rate, resolution, and aspect ratio. Compression reduces file sizes while maintaining quality. Frames are still images that make up video sequences. Frame rate determines smoothness. Resolution is pixels and quality. Aspect ratio is width to height ratio. Video can be compressed using intra-frame or inter-frame techniques. Enhancement improves quality using techniques like noise reduction and color correction. Analysis extracts information from video.
Video and animation involve capturing and displaying sequences of images to depict motion. Video uses real-world images while animation uses drawn or computer-generated images. MPEG standards like MPEG-1, 2, and 4 are used to compress digital video for storage and transmission by removing spatial and temporal redundancies between frames. MPEG compression involves three frame types - I, P, and B frames. I-frames are independent while P and B frames use motion prediction from previous and following frames. Grouping frames into GOPs allows efficient compression. The MPEG encoding and decoding process reconstructs frames using motion vectors and compensating for prediction errors.
This document discusses differences between film and digital formats, as well as key aspects of digital video such as resolution, aspect ratios, standard definition vs high definition, capture formats, online vs offline editing, linear vs non-linear editing, and the replacement of tapes with hard disks. Some of the main points made are that IMAX film has the highest quality, digital cameras can match film quality while being lighter, pixels determine resolution, and non-linear editing on hard disks is now easier and faster than linear tape-based editing.
This document discusses differences between film and digital formats, as well as key aspects of digital video such as resolution, aspect ratios, standard definition vs high definition, capture formats, online vs offline editing, linear vs non-linear editing, and the replacement of tapes with hard disks. Some of the main points made are that IMAX film has the highest quality, digital cameras can match film quality while being lighter, pixels determine resolution, and non-linear editing on hard disks is now easier and faster than linear tape-based editing.
This document discusses digital video, including its sources, types, and characteristics. Digital video combines graphics and audio to create dynamic content. It can originate from video cameras, film, or animation. There are different types of analog video formats like NTSC, PAL, and SECAM, as well as component video formats. Digital video solves issues with analog by providing an identical digital representation without generation loss. The main characteristics of digital video are frame rate, frame size, and color depth.
The document provides information on digital video, including quality factors, compression strategies, file formats, and guidelines for creating and using video in multimedia projects. It discusses screen resolution and frame rate as key quality factors that can be adjusted. Compression strategies like intra-frame, inter-frame, and variable bit rate encoding are described. The document outlines the process of creating original digital video, including shooting, editing, and rendering steps. It provides considerations for choosing digital video cameras and guidelines for video shooting. Editing software features and operations are defined. Rendering decisions around codec, resolution, frame rate, and other encoding options are also summarized.
Introduction to Digital Videos, Motion Estimation: Principles & Compensation. Learn more in IIT Kharagpur's Image and Video Communication online certificate course.
This document compares video compression standards MPEG-4 and H.264. It provides an overview of both standards, including their development histories and profiles. MPEG-4 was the first standard to support object-based video coding and compression of different media types. H.264 provides significantly better compression than prior standards like MPEG-2 at the cost of higher computational complexity. Both standards are widely used today for applications ranging from mobile and internet video to television broadcasting and digital cinema.
The document discusses digital image upscaling techniques from traditional methods to deep learning methods. It covers classical super-resolution methods for images and videos, including interpolation-based, edge-directed, frequency-domain, and example-based methods. It also explains the challenges of super-resolution such as information loss during the digital conversion process.
Video and animation involve capturing and displaying sequences of images to depict motion. Video uses real-world images while animation uses drawn or computer-generated images. MPEG standards like MPEG-1, 2, and 4 are used to compress digital video for storage and transmission by removing spatial and temporal redundancies between frames. MPEG compression involves three frame types - I, P, and B frames. I-frames are independent while P and B frames use motion prediction from previous and following frames. Grouping frames into GOPs allows efficient compression. The MPEG encoding and decoding process reconstructs frames using motion vectors and compensating for prediction errors.
This document discusses differences between film and digital formats, as well as key aspects of digital video such as resolution, aspect ratios, standard definition vs high definition, capture formats, online vs offline editing, linear vs non-linear editing, and the replacement of tapes with hard disks. Some of the main points made are that IMAX film has the highest quality, digital cameras can match film quality while being lighter, pixels determine resolution, and non-linear editing on hard disks is now easier and faster than linear tape-based editing.
This document discusses differences between film and digital formats, as well as key aspects of digital video such as resolution, aspect ratios, standard definition vs high definition, capture formats, online vs offline editing, linear vs non-linear editing, and the replacement of tapes with hard disks. Some of the main points made are that IMAX film has the highest quality, digital cameras can match film quality while being lighter, pixels determine resolution, and non-linear editing on hard disks is now easier and faster than linear tape-based editing.
This document discusses digital video, including its sources, types, and characteristics. Digital video combines graphics and audio to create dynamic content. It can originate from video cameras, film, or animation. There are different types of analog video formats like NTSC, PAL, and SECAM, as well as component video formats. Digital video solves issues with analog by providing an identical digital representation without generation loss. The main characteristics of digital video are frame rate, frame size, and color depth.
The document provides information on digital video, including quality factors, compression strategies, file formats, and guidelines for creating and using video in multimedia projects. It discusses screen resolution and frame rate as key quality factors that can be adjusted. Compression strategies like intra-frame, inter-frame, and variable bit rate encoding are described. The document outlines the process of creating original digital video, including shooting, editing, and rendering steps. It provides considerations for choosing digital video cameras and guidelines for video shooting. Editing software features and operations are defined. Rendering decisions around codec, resolution, frame rate, and other encoding options are also summarized.
Introduction to Digital Videos, Motion Estimation: Principles & Compensation. Learn more in IIT Kharagpur's Image and Video Communication online certificate course.
This document compares video compression standards MPEG-4 and H.264. It provides an overview of both standards, including their development histories and profiles. MPEG-4 was the first standard to support object-based video coding and compression of different media types. H.264 provides significantly better compression than prior standards like MPEG-2 at the cost of higher computational complexity. Both standards are widely used today for applications ranging from mobile and internet video to television broadcasting and digital cinema.
The document discusses digital image upscaling techniques from traditional methods to deep learning methods. It covers classical super-resolution methods for images and videos, including interpolation-based, edge-directed, frequency-domain, and example-based methods. It also explains the challenges of super-resolution such as information loss during the digital conversion process.
The document discusses various aspects of digital video editing technology, including:
1. Digital camera resolution and film sizes have improved over the years to provide better picture quality. The more pixels or a wider film size, the higher the quality.
2. Aspect ratios, such as 4:3, determine the width-to-height ratio of images. Video can be captured and displayed at different resolutions like standard definition or high definition.
3. Digital video editing allows for non-linear and flexible editing, where footage can be reordered and changed easily at any time without major issues.
06 13sept 8313 9997-2-ed an adaptive (edit lafi)IAESIJEECS
A robust Adaptive Reconstruction Error Minimization Convolution Neural Network (ARemCNN) architecture introduced to provide high reconstruction quality from low resolution using parallel configuration. Our proposed model can easily train the bulky datasets such as YUV21 and Videoset4.Our experimental results shows that our model outperforms many existing techniques in terms of PSNR, SSIM and reconstruction quality. The experimental results shows that our average PSNR result is 39.81 considering upscale-2, 35.56 for upscale-3 and 33.77 for upscale-4 for Videoset4 dataset which is very high in contrast to other existing techniques. Similarly, the experimental results shows that our average PSNR result is 38.71 considering upscale-2, 34.58 for upscale-3 and 33.047 for upscale-4 for YUV21 dataset.
The document discusses various aspects of video systems and design, including how video works, different broadcast standards, analog and digital video formats, video recording tape formats, shooting and editing video, and optimizing video files. It provides details on video compression standards like MPEG and considerations for integrating video into multimedia projects. Overall, the document serves as a guide for understanding video technology and best practices for using video effectively in multimedia design.
Video and television systems work by presenting a sequence of images rapidly enough that the human eye perceives them as continuous motion. Different regions use different television standards that determine aspects like the number of lines, frames per second, and color systems. Video compression codecs like MPEG remove spatial and temporal redundancy to greatly reduce file sizes for storage and transmission while maintaining adequate quality.
Presentation on HDTV Technology and Scanning Techniques
1) Progressive and interlaced
2) Broadcasting System
3) Aspect Ratio
4) Frame Rate
5) Pixels
6) Frame
7) CRT and HDTV
The document compares video compression standards MPEG-4 and H.264. It discusses key aspects of each including profiles, levels, uses and future applications. MPEG-4 introduced object-based coding while H.264 provides around 50% better compression than MPEG-4 at similar quality levels. Both standards are widely used for video streaming, television broadcasting, and storage applications like Blu-ray discs. Ongoing development aims to improve support for high definition video formats.
Training Videovigilancia IP: What, Why, When and HowNestor Carralero
Network cameras can compress video using codecs like H.264 to reduce file sizes. They support different resolutions, frame rates, and bit rates. Features like digital zoom, WDR, and privacy masks customize camera views. Audio uses codecs like AAC and AMR, and 2-way audio allows remote communication. Automatic settings like AES, AWB, and AGC adjust camera settings without manual control.
This document compares video compression standards MPEG-4 and H.264. It discusses key factors for video compression like spatial and temporal sampling. It provides an overview of MPEG-4 including object-based coding, profiles and levels. H.264 is introduced as a standard that provides 50% bit rate savings over MPEG-2. Profiles and levels are explained for both standards. Common uses of each are listed, along with future development options.
This document discusses two-dimensional wavelets for image processing. It explains that 2D wavelets can be constructed as separable products of 1D wavelets, using scaling functions and wavelet functions. The document provides examples of 2D Haar wavelets and discusses how a 2D wavelet decomposition breaks down the frequency content of an image into different subbands. It also summarizes applications of 2D wavelets such as image denoising, edge detection, and compression.
This document provides an overview of key concepts in multimedia systems including digital video formats, properties of video such as frame rate and aspect ratio, video compression techniques, and video production equipment and processes. It covers analog vs digital video, interlacing vs progressive scanning, common video file formats like AVI, MOV, and MPG, and how to transfer video from a camcorder to a computer.
Direct satellite broadcast receiver using mpeg 2arpit shukla
1) Direct-broadcast satellite (DBS) transmits satellite television signals for home reception and involves programming sources, broadcast centers, satellites, satellite dishes, and receivers.
2) Error correction in DBS uses interleaving and de-interleaving to improve burst error correction by distributing errors across codewords.
3) MPEG standards including MPEG-1, MPEG-2, and MPEG-4 are used for audio and video compression and transmission, with MPEG-2 widely used for digital television broadcast by satellite, cable, and terrestrial television systems.
This document provides an overview of digital video components and concepts. It discusses the differences between analog and digital video, factors that affect video quality like frame rate and resolution, video compression and file formats, and tools for video editing and playback. Key topics covered include video streaming, capture cards, and common software used for editing and playing digital video files.
Digital cameras work by focusing light onto an electronic sensor that converts the light information into digital data made up of ones and zeros. The sensor is either a CCD or CMOS chip. Key differences between digital and film cameras are that digital cameras have built-in computers to electronically record and store images, while film cameras rely on chemical and mechanical processes and do not require electricity. Resolution, focal length, storage, file format, and understanding of pixels are also important factors for digital cameras.
Creating 3D neuron reconstructions from image stacks and virtual slidesMBF Bioscience
This presentation was given at a workshop that focused on reconstructing neurons from image stacks to study neuron morphology. It covers strategies for capturing image stacks optimized for neuron reconstruction with Neurolucida 360, a new software product that makes it much easier to trace neurons from image stacks.
The document discusses key concepts for digital graphics in computer games such as pixel resolution, vector and raster images, file formats, compression techniques, image capture devices, optimizing performance, storage and asset management. It provides definitions and examples for each topic. The sources used to compile the information are cited at the end.
EMC 3130/2130 Lecture One - Image DigitalEdward Bowen
Video is composed of sequential still images called frames that are captured, stored, and played back rapidly to create the illusion of motion when accompanied by sound. Each frame consists of millions of electrically excitable pixels that are scanned from left to right and top to bottom. Higher frame rates and resolution provide smoother motion and more detail but require more storage space, so the video is compressed using a codec that removes redundant information while maintaining quality. This process converts the video into a digital file format contained within a wrapper.
A Segmentation based Sequential Pattern Matching for Efficient Video Copy De...SWAMI06
A considerable number of videos are illegal copies or manipulated versions of existing media, making copyright management a complicated process.
Call for Change:-
Today’s widespread video copyright infringement calls for the development of fast and accurate copy-detection algorithms.
As video is the most complex type of digital media, it has so far received the least attention regarding copyright management.
Protect Data:-
Content-based copy detection (CBCD) ,a promising technique for video monitoring and copyright protection.
This document discusses megapixel camera technology for network video surveillance systems. It begins by explaining how megapixel cameras can cover a larger area than VGA cameras, with a 1.3 megapixel camera covering an area 9 times as large as a VGA camera. It then discusses camera specifications like resolution, pixels, lens optics, and compression methods. Myths about megapixel cameras are debunked, and applications and considerations for deploying megapixel cameras are provided, such as calculating camera requirements, storage needs, and bandwidth requirements.
Digital video has replaced analog video as the preferred method for delivering multimedia content. Video files can be extremely large due to factors like frame rate, image size, and color depth. Common file formats for digital video include AVI, QuickTime, and MP4. Video editing software allows for nonlinear editing with features like transitions, effects, and sound synchronization. Compression techniques help reduce large file sizes, though some quality is lost with lossy compression.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
The document discusses various aspects of digital video editing technology, including:
1. Digital camera resolution and film sizes have improved over the years to provide better picture quality. The more pixels or a wider film size, the higher the quality.
2. Aspect ratios, such as 4:3, determine the width-to-height ratio of images. Video can be captured and displayed at different resolutions like standard definition or high definition.
3. Digital video editing allows for non-linear and flexible editing, where footage can be reordered and changed easily at any time without major issues.
06 13sept 8313 9997-2-ed an adaptive (edit lafi)IAESIJEECS
A robust Adaptive Reconstruction Error Minimization Convolution Neural Network (ARemCNN) architecture introduced to provide high reconstruction quality from low resolution using parallel configuration. Our proposed model can easily train the bulky datasets such as YUV21 and Videoset4.Our experimental results shows that our model outperforms many existing techniques in terms of PSNR, SSIM and reconstruction quality. The experimental results shows that our average PSNR result is 39.81 considering upscale-2, 35.56 for upscale-3 and 33.77 for upscale-4 for Videoset4 dataset which is very high in contrast to other existing techniques. Similarly, the experimental results shows that our average PSNR result is 38.71 considering upscale-2, 34.58 for upscale-3 and 33.047 for upscale-4 for YUV21 dataset.
The document discusses various aspects of video systems and design, including how video works, different broadcast standards, analog and digital video formats, video recording tape formats, shooting and editing video, and optimizing video files. It provides details on video compression standards like MPEG and considerations for integrating video into multimedia projects. Overall, the document serves as a guide for understanding video technology and best practices for using video effectively in multimedia design.
Video and television systems work by presenting a sequence of images rapidly enough that the human eye perceives them as continuous motion. Different regions use different television standards that determine aspects like the number of lines, frames per second, and color systems. Video compression codecs like MPEG remove spatial and temporal redundancy to greatly reduce file sizes for storage and transmission while maintaining adequate quality.
Presentation on HDTV Technology and Scanning Techniques
1) Progressive and interlaced
2) Broadcasting System
3) Aspect Ratio
4) Frame Rate
5) Pixels
6) Frame
7) CRT and HDTV
The document compares video compression standards MPEG-4 and H.264. It discusses key aspects of each including profiles, levels, uses and future applications. MPEG-4 introduced object-based coding while H.264 provides around 50% better compression than MPEG-4 at similar quality levels. Both standards are widely used for video streaming, television broadcasting, and storage applications like Blu-ray discs. Ongoing development aims to improve support for high definition video formats.
Training Videovigilancia IP: What, Why, When and HowNestor Carralero
Network cameras can compress video using codecs like H.264 to reduce file sizes. They support different resolutions, frame rates, and bit rates. Features like digital zoom, WDR, and privacy masks customize camera views. Audio uses codecs like AAC and AMR, and 2-way audio allows remote communication. Automatic settings like AES, AWB, and AGC adjust camera settings without manual control.
This document compares video compression standards MPEG-4 and H.264. It discusses key factors for video compression like spatial and temporal sampling. It provides an overview of MPEG-4 including object-based coding, profiles and levels. H.264 is introduced as a standard that provides 50% bit rate savings over MPEG-2. Profiles and levels are explained for both standards. Common uses of each are listed, along with future development options.
This document discusses two-dimensional wavelets for image processing. It explains that 2D wavelets can be constructed as separable products of 1D wavelets, using scaling functions and wavelet functions. The document provides examples of 2D Haar wavelets and discusses how a 2D wavelet decomposition breaks down the frequency content of an image into different subbands. It also summarizes applications of 2D wavelets such as image denoising, edge detection, and compression.
This document provides an overview of key concepts in multimedia systems including digital video formats, properties of video such as frame rate and aspect ratio, video compression techniques, and video production equipment and processes. It covers analog vs digital video, interlacing vs progressive scanning, common video file formats like AVI, MOV, and MPG, and how to transfer video from a camcorder to a computer.
Direct satellite broadcast receiver using mpeg 2arpit shukla
1) Direct-broadcast satellite (DBS) transmits satellite television signals for home reception and involves programming sources, broadcast centers, satellites, satellite dishes, and receivers.
2) Error correction in DBS uses interleaving and de-interleaving to improve burst error correction by distributing errors across codewords.
3) MPEG standards including MPEG-1, MPEG-2, and MPEG-4 are used for audio and video compression and transmission, with MPEG-2 widely used for digital television broadcast by satellite, cable, and terrestrial television systems.
This document provides an overview of digital video components and concepts. It discusses the differences between analog and digital video, factors that affect video quality like frame rate and resolution, video compression and file formats, and tools for video editing and playback. Key topics covered include video streaming, capture cards, and common software used for editing and playing digital video files.
Digital cameras work by focusing light onto an electronic sensor that converts the light information into digital data made up of ones and zeros. The sensor is either a CCD or CMOS chip. Key differences between digital and film cameras are that digital cameras have built-in computers to electronically record and store images, while film cameras rely on chemical and mechanical processes and do not require electricity. Resolution, focal length, storage, file format, and understanding of pixels are also important factors for digital cameras.
Creating 3D neuron reconstructions from image stacks and virtual slidesMBF Bioscience
This presentation was given at a workshop that focused on reconstructing neurons from image stacks to study neuron morphology. It covers strategies for capturing image stacks optimized for neuron reconstruction with Neurolucida 360, a new software product that makes it much easier to trace neurons from image stacks.
The document discusses key concepts for digital graphics in computer games such as pixel resolution, vector and raster images, file formats, compression techniques, image capture devices, optimizing performance, storage and asset management. It provides definitions and examples for each topic. The sources used to compile the information are cited at the end.
EMC 3130/2130 Lecture One - Image DigitalEdward Bowen
Video is composed of sequential still images called frames that are captured, stored, and played back rapidly to create the illusion of motion when accompanied by sound. Each frame consists of millions of electrically excitable pixels that are scanned from left to right and top to bottom. Higher frame rates and resolution provide smoother motion and more detail but require more storage space, so the video is compressed using a codec that removes redundant information while maintaining quality. This process converts the video into a digital file format contained within a wrapper.
A Segmentation based Sequential Pattern Matching for Efficient Video Copy De...SWAMI06
A considerable number of videos are illegal copies or manipulated versions of existing media, making copyright management a complicated process.
Call for Change:-
Today’s widespread video copyright infringement calls for the development of fast and accurate copy-detection algorithms.
As video is the most complex type of digital media, it has so far received the least attention regarding copyright management.
Protect Data:-
Content-based copy detection (CBCD) ,a promising technique for video monitoring and copyright protection.
This document discusses megapixel camera technology for network video surveillance systems. It begins by explaining how megapixel cameras can cover a larger area than VGA cameras, with a 1.3 megapixel camera covering an area 9 times as large as a VGA camera. It then discusses camera specifications like resolution, pixels, lens optics, and compression methods. Myths about megapixel cameras are debunked, and applications and considerations for deploying megapixel cameras are provided, such as calculating camera requirements, storage needs, and bandwidth requirements.
Digital video has replaced analog video as the preferred method for delivering multimedia content. Video files can be extremely large due to factors like frame rate, image size, and color depth. Common file formats for digital video include AVI, QuickTime, and MP4. Video editing software allows for nonlinear editing with features like transitions, effects, and sound synchronization. Compression techniques help reduce large file sizes, though some quality is lost with lossy compression.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
2. Introduction
• Video processing is the manipulation and analysis of digital video
sequences.
• Basic video processing techniques include trimming, image resizing,
brightness and contrast adjustment, fade in and fade out, and analyzing.
• These tasks can be performed using a variety of ML techniques, including
deep learning, computer vision, and natural language processing.
3. Formats
• MP4 - commonly used for video compression.
• AVI - container format that was developed by Microsoft
• MOV - developed by Apple
• AVCHD - commonly used for video recorded by digital camcorders
and DSLR cameras.
• FLV - used for streaming video over the internet. FLV is commonly
used for videos on websites such as YouTube and Vimeo.
4. Key Concepts
• Compression - Compression is the process of reducing the size of a video file
while maintaining its quality. Video compression algorithms remove redundant
information.
• Frame - a frame refers to a single still image that makes up a sequence of images
(or "frames") that, when played back in rapid succession, create the illusion of
motion
• Frame rate- The frame rate is the number of frames displayed per second. It
determines the smoothness of the video.
• Resolution - Resolution refers to the number of pixels in a video frame. A higher
resolution means more pixels and better quality.
• Aspect ratio - The aspect ratio is the ratio of the width of a video frame to its
height. Common aspect ratios include 4:3 and 16:9
5. Video Processing Techniques
• Compression: to reduce the size of a video file while maintaining its
quality.
• Enhancement: to improve the visual quality of a video, such as noise
reduction, color correction, and sharpening.
• Restoration: to repair or improve the quality of a video that has been
degraded by noise, blur, or other factors.
• Analysis: to extract information from video sequences, such as object
tracking, facial recognition, and scene analysis.
6. Video Compression
• Inter-frame compression is a technique that reduces the amount of data
needed to represent a video by only storing the differences between
consecutive frames, instead of storing each frame in its entirety.
• This is done by comparing each frame to the preceding one and only
storing the changes, rather than the entire frame.
• Most commonly used algorithms are H.264, VP9, HEVC , etc..
7. Video Compression
• Intra-frame compression, also known as intra-coded compression,
works by compressing each frame individually.
• It uses techniques such as Discrete Cosine Transform (DCT) and
Color Quantization to compress the data of each frame.
• Discrete Cosine - technique applied to image pixels in spatial domain
in order to transform them into a frequency domain in which
redundancy can be identified
• Quantization - the process of mapping continuous infinite values to a
smaller set of discrete finite values.
8. Video Compression
• Lossy compression is a technique that removes some of the data from
the video in order to reduce its file size.
• This can be done in various ways, such as by removing redundant data
or by removing information that the human eye is less likely to notice.
• The result is a lower quality video but with a smaller file size.
• It's worth noting that most of the existing video compression standard
use inter-frame compression as it's more efficient than intra-frame
compression
9. Video Compression
• Fractal Compression: This technique uses fractal mathematics to
compress an image. The image is broken down into smaller fractal
patterns, which can be used to recreate the original image with a
smaller file size.
• Vector Quantization: This technique groups similar image features
together and replaces them with a single symbol. This reduces the
amount of data needed to represent the image and can be applied on
both grayscale and color images.
• Run-Length Encoding: This technique is used for images with large
areas of uniform color. It replaces repeating pixels with a single
symbol, reducing the amount of data needed to represent the image.
10. Video Enhancement Techniques
• Super-resolution: increases the resolution of a video by estimating and
reconstructing high-resolution details from low-resolution frames.
• Denoising: reduces noise in a video by removing or reducing random
variations in pixel intensity.
• Color correction: improves the color accuracy of a video by adjusting the color
balance, saturation, and brightness.
• Deblurring: removes blur from a video caused by camera shake or fast-moving
objects.
• Stabilization: removes jitter or shake from a video caused by camera
movement.
11. Video Super Resolution
• Video super-resolution (VSR) is a technique used to increase the resolution of a video by
estimating and reconstructing high-resolution details from low-resolution frames. Some
common video super-resolution techniques include:
• Interpolation-based methods: These methods use interpolation algorithms such as bicubic
or Lanczos to estimate missing pixels in the high-resolution version of the video.
• Reconstruction-based methods: These methods use image or video processing techniques
to reconstruct the high-resolution version of the video. Examples of these methods
include single image super-resolution (SISR) and video super-resolution (VSR).
• Deep learning-based methods: These methods use deep neural networks (DNNs) to learn
the mapping between low-resolution and high-resolution images. Examples of these
methods include deep convolutional neural networks (CNNs) and generative adversarial
networks (GANs).
• Hybrid methods : These methods combine multiple techniques to achieve the best results.
For example, combining deep learning with interpolation or reconstruction-based
methods.
12. Interpolation
• Interpolation-based methods for video super-resolution (VSR) use interpolation algorithms to
estimate missing pixels in the high-resolution version of the video. Some common interpolation-
based VSR methods include:
• Nearest-neighbor interpolation: This method replicates the value of the nearest pixel to fill in
missing pixels. It is simple to implement but can introduce "blocky" artifacts in the output.
• Bilinear interpolation: This method uses the weighted average of the four closest pixels to estimate
the value of missing pixels. It is a more sophisticated method than nearest-neighbor interpolation
but can still introduce some artifacts in the output.
• Bicubic interpolation: This method uses the weighted average of the 16 closest pixels to estimate
the value of missing pixels. It is more sophisticated than bilinear interpolation and typically
produces better results, but it is also more computationally expensive.
• Lanczos interpolation: This method uses a sinc function to estimate the value of missing pixels. It
is a highly sophisticated method and is known for producing the best results among interpolation-
based methods, but it is also the most computationally expensive.
13. Reconstruction-based Methods
• Reconstruction-based methods for video super-resolution (VSR) use
image or video processing techniques to reconstruct the high-
resolution version of the video.
• These methods typically involve building a model of the image or
video and using that model to generate the high-resolution version.
• Some common reconstruction-based VSR methods include:
• Optical flow-based methods: These methods use the motion information
between the frames to estimate the high-resolution frame.
• Spatial-Temporal Super-Resolution methods: These methods use a
combination of spatial and temporal information in order to increase the
resolution of the video.
14. Optical Flow-based Methods
• Optical flow-based methods for video super-resolution (VSR) use the motion information
between frames to estimate the high-resolution frame.
• These methods work by estimating the motion vectors between low-resolution frames and
using these vectors to warp the pixels of one frame to match the position of the pixels in
another frame.
• The high-resolution frame can then be reconstructed by combining the warped frames.
• Optical flow-based VSR methods typically involve the following steps:
• Estimating the optical flow: This step involves estimating the motion vectors between low-
resolution frames using techniques such as Lucas-Kanade, Horn-Schunck or deep learning-based
optical flow estimation.
• Warping frames: This step involves using the motion vectors to warp the pixels of one frame to
match the position of the pixels in another frame.
• Combining frames: This step involves combining the warped frames to form the high-resolution
frame. This can be done by averaging, weighted averaging, or median filtering the warped frames.
15. Spatial-Temporal Super-Resolution
• Spatial-Temporal Super-Resolution (STSR) is a method that combines spatial and
temporal information in order to increase the resolution of a video.
• These methods utilize both the spatial information of the individual frames and the
temporal information between frames to generate high-resolution video.
• STSR methods typically involve the following steps:
• Spatial resolution enhancement: This step involves enhancing the resolution of each individual
frame of the video using interpolation-based methods, SISR or DNN-based methods.
• Temporal information extraction: This step involves extracting temporal information from the
video, such as motion vectors or optical flow, that can be used to align the frames and improve the
resolution of the video.
• Temporal resolution enhancement: This step involves using the extracted temporal information to
align and fuse the frames to generate the high-resolution video.
• STSR methods can be effective for VSR, especially when applied to videos with complex
temporal dynamics such as fast moving objects or complex background. These methods
can also be robust to occlusions and motion discontinuities. However, these methods can
be computationally expensive, especially when extracting temporal information.
16. Denoising in Video Enhancement
• Denoising in video enhancement is the process of removing noise from a video in
order to improve its visual quality.
• Noise in videos can be caused by various factors such as low-light conditions,
electronic noise in the camera sensor, or compression artifacts.
• There are several methods for denoising videos, including:
• Spatial filtering: This method involves applying a filter to each frame of the video to reduce
noise. Examples of spatial filters include median filters and Gaussian filters.
• Temporal filtering: This method involves using information from multiple frames of the video
to reduce noise. Examples of temporal filters include Kalman filters and recursive filters.
• Non-local Means filter: This method is a spatial-temporal filter that uses information from
similar pixels in other frames to remove noise.
• Deep learning-based methods: These methods use deep neural networks (DNNs) to learn the
mapping between noisy and denoised videos. Examples of DNN-based denoising methods
include autoencoder-based and UNet-based methods.
• Hybrid methods: These methods combine spatial, temporal, and deep learning-based methods
to denoise videos.
17. Spatial Filtering
• Spatial filtering is a method for denoising videos that involves applying a filter to each
frame of the video to reduce noise.
• These filters operate on the spatial domain, meaning that they process the pixels in each
frame independently of the pixels in other frames.
• Spatial filtering methods are fast and easy to implement, and can be useful for removing
noise such as sensor noise, impulse noise, or salt-and-pepper noise.
• Some examples of spatial filters include:
• Median filter: This filter replaces the value of a pixel with the median value of the pixels in a
neighborhood around it. It is effective at removing salt-and-pepper noise but can blur fine details.
• Gaussian filter: This filter replaces the value of a pixel with a weighted average of the pixels in a
neighborhood around it, where the weighting is determined by a Gaussian function. It is effective at
removing Gaussian noise but can blur fine details.
• Mean filter: This filter replaces the value of a pixel with the mean value of the pixels in a
neighborhood around it. It is effective at removing impulse noise but can blur fine details.
• Bilater filter: This filter is a combination of a Gaussian filter and a mean filter. It smooths the image
while preserving the edges.
18. Temporal Filtering
• Temporal filtering is a method for denoising videos that involves using information from multiple
frames of the video to reduce noise.
• These filters operate in the temporal domain, meaning that they process the pixels in each frame in
relation to the pixels in other frames.
• Temporal filtering methods can be more effective at removing noise such as temporal noise (noise
that changes over time), camera shake, or compression artifacts.
• Some examples of temporal filters include:
• Kalman filter: This filter uses a mathematical model to estimate the state of a system over time, and is used to
predict the current frame based on the previous frames. It can be effective at removing temporal noise, but can
be computationally expensive.
• Recursive filter: This filter uses recursive algorithms to estimate the current frame based on the previous
frames. It is similar to the Kalman filter but more computationally efficient.
• Optical flow-based filter: This filter uses optical flow to align frames, and then uses spatial filtering to remove
noise. It can be effective at removing noise caused by camera shake but can be sensitive to occlusions and
motion discontinuities.
• Recurrent neural networks (RNN): This filter uses a recurrent neural network to estimate the current frame
based on the previous frames. It is similar to the Kalman filter but uses a deep learning approach, can be more
powerful in removing noise and can be computationally expensive.
19. The Non-local Means
• The Non-local Means (NLM) filter is a method for denoising videos that uses
information from similar pixels in other frames to remove noise.
• It is a spatial-temporal filter, meaning that it processes the pixels in each frame in
relation to the pixels in other frames and in a neighborhood around them.
• The NLM filter operates in the following steps:
• For each pixel in the current frame, it searches for similar pixels in other frames.
• It computes a weighted average of the similar pixels, where the weighting is determined by a
similarity metric such as the Euclidean distance.
• It replaces the value of the current pixel with the computed weighted average.
• The NLM filter is effective at removing noise such as temporal noise, camera
shake, and compression artifacts. It can also preserve edges and fine details better
than spatial filters. However, it can be computationally expensive, as it requires
searching for similar pixels in other frames.
20. Color correction
• Color correction is the process of adjusting the colors of a video to improve its visual
quality. It can be used to correct color imbalances, fix exposure issues, and improve the
overall color and tone of the video. There are several techniques that can be used for
color correction:
1.White balance: This technique is used to correct the color cast of a video caused by
different lighting conditions. It can be done by adjusting the color temperature of the video
to make it appear more neutral.
2.Color grading: This technique is used to adjust the overall color and tone of a video. It can
be done by adjusting the brightness, contrast, saturation, and hue of the video.
3.Curves: This technique allows for fine-grained color correction by adjusting the brightness
levels of individual colors.
4.LUTs (Lookup tables): A LUT is a predefined table that maps the input colors to output
colors. Using a LUT allows for fast and consistent color correction across multiple shots.
5.Color matching: This technique is used to match the colors of different shots or scenes. It
can be done by adjusting the colors of one shot to match the colors of another shot.
6.Machine Learning-based methods: These methods use machine learning algorithms to
learn the underlying structure of the video, and then use this knowledge to correct the
color.
21. Deblurring:
• Deblurring, also known as image restoration, is the process of removing blur from an image or video
caused by factors such as camera shake, fast motion, or a small aperture. There are several
techniques that can be used for deblurring:
1. Inverse Filtering: This technique uses a known blur function to reverse the blurring effect. This
method is highly sensitive to noise and is usually not used in practice.
2. Wiener Filtering: This technique uses a statistical model of the image and the blur function to
estimate the original image. This method is less sensitive to noise but can still produce poor results.
3. Blind Deconvolution: This technique is used when the blur function is not known. It attempts to
estimate both the blur function and the original image simultaneously. This method can produce
good results but is highly sensitive to noise and initialization.
4. Regularization-based methods: These methods add a regularization term to the objective function
to prevent overfitting. Examples include Tikhonov regularization, Total Variation regularization, and
Sparse Representation based methods.
5. Machine Learning-based methods: These methods use machine learning algorithms such as Deep
Learning to learn the underlying structure of the image and then use this knowledge to deblur the
image.
22. Stabilization
• Video stabilization is the process of removing the unwanted camera shake or jitter from a
video. It is used to make the video appear smoother and more stable. There are several
techniques that can be used for video stabilization:
1.Optical Flow: This technique uses the motion of the pixels between consecutive frames to
estimate the camera motion. The video is then compensated for this motion by aligning
the frames.
2.Feature-based: This technique uses the features such as points, edges or corners in the
video to estimate the camera motion. These features are tracked between consecutive
frames to estimate the motion.
3.Hybrid methods: These methods combine the above techniques. They first use feature-
based methods to estimate the motion, then use optical flow to refine the motion estimate.
4.Gyroscopic stabilization: This technique uses a gyroscopic sensor to measure the rotation
of the camera. The video is then compensated for this rotation by aligning the frames.
5.Machine Learning-based methods: These methods use machine learning algorithms such
as Deep Learning to learn the underlying structure of the video and then use this
knowledge to stabilize the video.
23. Segmentation
• Two are the most widely used segmentation techniques
• Semantic segmentation: This involves dividing a video into segments
based on semantic content, such as by identifying and separating
different objects or regions in a video, and then classifying them into
semantic categories.
• Motion segmentation: This involves dividing a video into segments
based on motion, such as by identifying and separating different
moving objects or regions in a video.
24. RCNN
• Generate initial sub-
segmentation, we generate many
candidate regions
• Use greedy algorithm to
recursively combine similar
regions into larger ones
• Use the generated regions to
produce the final candidate
region proposals
25. Fast-rcnn
• The same author of the previous
paper(R-CNN) solved some of the
drawbacks of R-CNN to build a faster
object detection algorithm and it was
called Fast R-CNN.
• The approach is similar to the R-CNN
algorithm.
• But, instead of feeding the region
proposals to the CNN, we feed the
input image to the CNN to generate a
convolutional feature map.
26. Faster-rcnn
• R-CNN & Fast R-CNN uses
selective search to find out the
region proposals.
• In faster-rcnn, Lets the network
learn the region proposals.
27. background subraction
• Frame differencing: Compares each frame to the
previous frame and detects changes.
• Running average: Keeps a running average of the
background and detects changes that deviate
from the average.
• Gaussian mixture model: Uses a statistical model
to represent the background and detect changes.
28. Optical flow and Clustering-based methods
• Optical flow algorithms compute the motion of each pixel in the
image by analyzing the changes in the pixel's position and color
from one frame to the next.
• Clustering-based methods involve grouping pixels or regions use
a set of features, such as color, texture, or motion information, to
represent the pixels or regions, and then apply a clustering
algorithm to group similar features together.
• popular clustering algorithms used for motion segmentation
include k-means, mean-shift, and Gaussian mixture models.
29. Video content Analysis
• Video content analysis deals with the extraction of metadata
from raw video to be used as components for further processing
in applications such as search, summarization, classification or
event detection.
• The main goal of video analytics is to automatically recognize
temporal and spatial events in videos.
• This technical capability is used in a wide range of domains including
entertainment, video retrieval and video browsing, health-care, retail,
automotive, transport, home automation, flame and smoke
detection, safety, and security
30. How does video analytics work?
• Video content analysis can be done in two different ways:
i. In real time, by configuring the system to trigger alerts for specific events
and incidents that unfold in the moment.
ii. In post processing, by performing advanced searches to facilitate forensic
analysis tasks.
• Feeding the system:The data being analyzed can come from various
streaming video sources. The most common are CCTV cameras, traffic
cameras and online video feeds.
• A key goal is coverage: we need to have a clear view of the entire area, and
from various angles,
31. Central processing vs edge
processing
• Video analysis software can be run centrally on servers that are generally
located in the monitoring station, which is known as central processing.
• Or, it can be embedded in the cameras themselves, a strategy known as edge
processing.
• With a hybrid approach, the processing performed by the cameras reduces
the data being processed by the central servers
35. Facial Recognition in Video
Analysis
• Facial recognition systems that can identify or
verify a person from a digital image or video
find application in a variety of contexts.
• Facial recognition works in two parts: face
detection and face identification.
i. In the first stage, the system detects faces
in the input data using methods like
background subtraction.
ii. Next, it measures the facial features to
define facial landmarks and tries to match
them with a known dataset. Based on the
percentage of accuracy of match, the faces
can be recognized or classified as unknown.
36. • Dlib’s face landmark predictor to detect a face
and extract features such as eyes, mouth,
brows, nose, and jawline.
• The image was standardized by cropping to
include just these features and aligning it
based on the location of eyes and the bottom
lip.
• The preprocessed image was then mapped to a
numerical vector representation. An algorithmic
comparison of the vector images made facial
recognition possible.
37. Detecting Motion
• We compare each frame of a video stream to the previous one
and detect all spots that have changed.
• We convert the image to gray and smooth it out a bit by blurring
the image. Converting to grey converts all RGB pixels to a value
between 0 and 255 where 0 is black and 255 is white.
38. • We’ll compare the previous frame with the current one by
examining the pixel values. Remember that since we’ve
converted the image to grey all pixels are represented by a
single value between 0 and 255.
• We use Threshold function “cv2.threshold” to convert each
pixel to either 0 (white) or 1 (black). The threshold for this is 20.
39. • Finding areas and contouring: We want to find the area that
has changed since the last frame, not each pixel. In order to do
so, we first need to find an area.
• cv.findContours it retrieves contours or outer limits from each
white spot from the part above.