Mathematics in the Digital Age
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This document discusses how digital technologies like bytes, kilobytes, megabytes, and gigabytes can be used to motivate mathematics concepts. It provides examples of the storage required for different types of digital files and exercises involving calculating file sizes and download speeds. The document also discusses how digital images can be represented using matrices and manipulated using matrix algebra operations.
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This document provides an overview of how data is represented in computers using binary and bytes. It explains that all data, including integers, characters, pictures, sounds and computer programs, is broken down and stored as sequences of binary digits (bits) that represent the on/off states of switches. Bytes, which are 8 bits each, are used to store a single character, pixel, or other basic unit of different data types. The document includes examples of how integers, characters, images and sound waves are converted into and represented by bytes to be processed by the computer.
Laureate Online Education Internet and Multimedia Technolog.docx
Laureate Online Education Internet and Multimedia Technology
© All right reserved, 2000-2008. The Internet and Multimedia Technology module, in all its
parts: syllabus, guidelines, lectures, discussion questions, technical notes, images, labs,
projects and any additional material is copyrighted by Laureate Online Education B.V.
Module: MSC MD (INTMMT) Seminar 3
Bitmaps and Colour
Introduction
We will turn our attention to Bitmaps and Colour during this Seminar. As we did
in Seminar 2, we will again recommend some applications and encourage you to
experiment.
Bitmapped images have none of the complexity of 3D graphics and yet they are
the final output from a 3D system. Often the 2D image desired will first be
created in 3D and then, after the perspective transformation is applied and it
becomes 2D, modified and enhanced. Let us take a look at some ideas
associated with Bitmaps.
We start with the resolution or measure of the granularity of the image. It is a
common error to assume that an image having 640 by 480 pixels on the screen
cover s only that number of phosphor dot triads on the face of the monitor. A
screen may state it provides so many dots per inch as its resolution. This is a
measure of the manufacturing quality. Depending on the size of the screen we
will have a different number of dots per pixel for a given computer resolution. It is
also rather short sighted to keep an image at a small resolution since it will look
very ‘blocky’ if enlarged or viewed at a greater resolution than originally stored.
This gives rise to large files being kept just in case we need the resolution! One
obvious way around this is to compress the image.
Compression schemes are considered to be of two types; Lossy and Lossless.
With lossless compression we can always regain the original quality whereas
with lossy compression we sacrifice some aspect of quality depending on the
application. When dealing with multimedia and the Internet we are very
concerned to obtain the best compression ration for a given quality of service
(QoS). The compression ratio is given by;
Compression Ratio = Uncompressed file size
Compressed file size
Most compression schemes rely on taking advantage of redundancy in the data
(in our case, the stream of pixels which make up the image). This takes many
forms. If we examine one scan line of an image we will notice that many pixels
1
are the same colour so stating for example Red 100 times is wasteful. It is
obviously shorter to store the value for red and its length. This is the basic idea
behind Run Length Encoding (RLE). A simple compression related to colour is to
appreciate that we do not need the full range twenty four bits can provide. If we
sample the image for the 256 commonest shades then we can reduce the
storage for colour by two thirds by sacrificing fine shading differences. A further
approach is useful with video and makes use of the ...
Canny Edge Detection Algorithm on FPGA
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the high-level implementation of the Canny algorithm using Simulink. The design and system-level block diagram of the implementation on an FPGA is shown, including loading an input image and displaying the output. Simulation and synthesis results are presented, showing the resource utilization on a Spartan 3E FPGA board. The implementation provides real-time edge detection to interface an FPGA with a monitor.
C010111519
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the Canny edge detection algorithm and its benefits. The document outlines the high-level implementation in Simulink and shows the input, grayscaled, and edge detected output images. It presents the system design with the FPGA reading in an image file and performing Canny edge detection. Simulation and synthesis results are shown verifying the design works as intended. The paper concludes the Canny edge detection algorithm was successfully designed, simulated, tested and realized on an FPGA.
Canny Edge Detection Algorithm on FPGA
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the Canny edge detection algorithm and its benefits. The document outlines the high-level implementation in Simulink and shows the input, grayscaled, and edge detected output images. It presents the system design with the FPGA reading in an image file and performing Canny edge detection. Simulation and synthesis results are shown verifying the design works as intended. The paper concludes the Canny edge detection algorithm was successfully designed, simulated, tested and realized on an FPGA.
Digital Image Processing
This document is a mini project report on digital image processing using MATLAB. It discusses various image processing techniques and applications implemented in MATLAB, including image formats, operations, and tools. Applications demonstrated include text recognition, color tracking, solving an engineering problem using image processing, creating a virtual slate using laser tracking, face detection, and distance estimation. The report provides examples of MATLAB functions used for tasks like importing, displaying, converting and cropping images, as well as analyzing and manipulating them.
Image compression jpeg coding standards
Image compression uses four stages to reduce file sizes: 1) transforming RGB pixels to the YCbCr color space, 2) applying discrete cosine transformation to concentrate pixel data into a few matrix elements, 3) quantizing pixel values to reduce their range, and 4) using Huffman coding to assign shorter bit codes to more common pixel values. This allows JPEG images to be compressed to smaller file sizes for storage and transmission while still maintaining good visual quality.
Basic of computer graphic - Computer Graphic - Notes
Computer Graphics is a sub-field of computer science and is concerned with digitally synthesizing and manipulating visual content.
OR
Computer Graphics is the study of techniques to improve communication between human and machine.
The word computer graphics means picture , graph or sense is dream with the help of computer.
Lossless Huffman coding image compression implementation in spatial domain by...
This document discusses lossless Huffman coding image compression using different block sizes and codebook sizes. It begins by introducing Huffman coding and image compression techniques. It then describes the methodology used, which involves reading an image in MATLAB, converting it to grayscale, extracting blocks from the image, quantizing the blocks using Huffman coding, and reconstructing the compressed image. 8 different compression scenarios are tested using various block and codebook sizes. The best scenario used a block size of 16 and codebook size of 50. Performance metrics like compression ratio, bit rate, PSNR, MSE and SNR are calculated. Enhancement techniques like Laplacian of Gaussian filtering and pseudo-coloring are then applied to the reconstructed image from the best
Overview of graphics systems.ppt
This document provides an overview of computer graphics systems. It discusses the basic components of a graphics system including input, computation, and output. For input, it describes common devices like mice, keyboards, and scanners. The computation stage involves transformations and rasterization to generate images. For output, it explains framebuffers, bit depths, and different display technologies like CRT, LCD, and projection displays. It provides details on how these displays work and their advantages/disadvantages.
new ai techniques.pptx
This document discusses the basics of computer graphics. It outlines the advantages of computer graphics such as producing high quality images and animation. It also classifies computer graphics systems as either interactive or passive. Interactive systems allow two-way communication between the user and computer while passive systems do not. The document then discusses pixels, color depth, frame buffers, and monitors. It concludes by outlining major areas of computer graphics like display of information, design/modeling, simulation, and user interfaces.
B070306010
This document discusses image compression algorithms using the Lapped Orthogonal Transform (LOT) and Discrete Cosine Transform (DCT) under the JPEG standard. It begins with an introduction to image compression and classification of compression schemes. It then describes LOT and DCT in detail and proposes a hybrid algorithm using both transforms simultaneously. The algorithm is tested on an image and achieves a peak signal-to-noise ratio of 36.76 decibels at a bit rate of 0.6 bits per pixel, providing higher quality than DCT alone. The document concludes the hybrid approach offers better energy compaction and quality at low bit rates than DCT.
Image compression
The document provides information on various techniques for image compression, including lossless and lossy compression methods. For lossless compression, it describes run-length encoding, entropy coding, and area coding. For lossy compression it discusses reducing the color space, chroma subsampling, and transform coding using DCT and wavelets. It also covers segmentation/approximation methods, spline interpolation, fractal coding, and bit allocation techniques for optimal compression.
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Laureate Online Education Internet and Multimedia Technolog.docx
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Basic of computer graphic - Computer Graphic - Notes
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