Bt9301 computer graphics (1)
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This document presents a comparison of two image inpainting techniques - curvature driven diffusion (CDD) inpainting and total variation (TV) inpainting. The paper aims to apply these two inpainting methods to grayscale and color images to restore damaged regions. CDD inpainting works by solving partial differential equations of isophote intensity, while TV inpainting is based on texture filling. Experimental results on various images are shown to demonstrate the effectiveness of the two approaches. The document also discusses related work, provides implementation details of the two methods, and outlines potential future work including hardware implementation.
Images in matlab
This document discusses working with images in MATLAB. It defines what an image is as a set of pixel intensity data stored in a 3D matrix with planes for red, green, and blue values. Popular image functions like imread, imshow, rgb2gray and imhist are introduced. Examples are given for loading an image, displaying it, converting it to grayscale, and viewing its histogram. Further image adjustments like contrast ratio changes and conversions to black and white or other formats are demonstrated.
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The document discusses different types of images in Matlab including binary, grayscale, indexed, and RGB images. It also summarizes commands to convert between image types such as converting grayscale to indexed or truecolor to binary. Finally, it provides examples of how to view images, measure pixel values and distances, and crop images using the imtool command.
Bt9301, computer graphics
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
Digital image processing lab 1
Digital Image Processing (Lab 1)
Course Objectives: To learn the fundamental concepts of Digital Image Processing and to study basic image processing operations.
Image compression models
This document discusses data compression techniques for digital images. It explains that compression reduces the amount of data needed to represent an image by removing redundant information. The compression process involves an encoder that transforms the input image, and a decoder that reconstructs the output image. The encoder uses three main stages: a mapper to reduce interpixel redundancy, a quantizer to reduce accuracy and psychovisual redundancy, and a symbol encoder to assign variable-length codes to the quantized values. The decoder performs the inverse operations of the encoder and mapper to reconstruct the original image, but does not perform the inverse of quantization which is a lossy process.
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G04654247
This document presents a comparison of two image inpainting techniques - curvature driven diffusion (CDD) inpainting and total variation (TV) inpainting. The paper aims to apply these two inpainting methods to grayscale and color images to restore damaged regions. CDD inpainting works by solving partial differential equations of isophote intensity, while TV inpainting is based on texture filling. Experimental results on various images are shown to demonstrate the effectiveness of the two approaches. The document also discusses related work, provides implementation details of the two methods, and outlines potential future work including hardware implementation.
Images in matlab
This document discusses working with images in MATLAB. It defines what an image is as a set of pixel intensity data stored in a 3D matrix with planes for red, green, and blue values. Popular image functions like imread, imshow, rgb2gray and imhist are introduced. Examples are given for loading an image, displaying it, converting it to grayscale, and viewing its histogram. Further image adjustments like contrast ratio changes and conversions to black and white or other formats are demonstrated.
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This document compares two image inpainting algorithms: the Fast Marching Method (FMM) and exemplar-based image inpainting. FMM uses structural consistency to fill damaged regions, while exemplar-based uses both structural and textural consistency. FMM is faster but does not preserve edges as well as exemplar-based. Exemplar-based works for both small and large regions but is slower. Both algorithms were tested on photos for tasks like removing objects or adding effects. Exemplar-based was better for large regions and edge preservation, while FMM was better for speed and small regions.
Perimetric Complexity of Binary Digital Images
Perimetric complexity is a measure of the complexity of binary pictures. It is defined as the sum of inside and outside perimeters of the foreground, squared, divided by the foreground area, divided by . Difficulties arise when this definition is applied to digital images composed of binary pixels. In this article we identify these problems and propose solutions. Perimetric complexity is often used as a measure of visual complexity, in which case it should take into account the limited resolution of the visual system. We propose a measure of visual perimetric complexity that meets this requirement.
Matlab Working With Images
The document discusses different types of images in Matlab including binary, grayscale, indexed, and RGB images. It also summarizes commands to convert between image types such as converting grayscale to indexed or truecolor to binary. Finally, it provides examples of how to view images, measure pixel values and distances, and crop images using the imtool command.
Bt9301, computer graphics
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
Digital image processing lab 1
Digital Image Processing (Lab 1)
Course Objectives: To learn the fundamental concepts of Digital Image Processing and to study basic image processing operations.
Image compression models
This document discusses data compression techniques for digital images. It explains that compression reduces the amount of data needed to represent an image by removing redundant information. The compression process involves an encoder that transforms the input image, and a decoder that reconstructs the output image. The encoder uses three main stages: a mapper to reduce interpixel redundancy, a quantizer to reduce accuracy and psychovisual redundancy, and a symbol encoder to assign variable-length codes to the quantized values. The decoder performs the inverse operations of the encoder and mapper to reconstruct the original image, but does not perform the inverse of quantization which is a lossy process.
www.ijerd.com
This document discusses using density plots generated in Mathematica to create innovative graphic designs suitable for the textile industry. Density plots arrange shades or colors in a rectangular mesh, making them well-suited for implementation on looms. The author explores using mathematical functions to generate designs with unusual 3, 5, or 7-fold symmetries instead of the typical 4, 6, or 8-fold patterns. Examples of designs include those based on complex number roots that exhibit different symmetrical patterns. Fractal functions are also investigated as a source for unique textile patterns.
Introductory Digital Image Processing using Matlab, IIT Roorkee
Introduction to Digital Image Processing using Matlab, IIT Roorkee. Organized by Electronics Section, Hobbies Club.
Reference: Gonzalez and Woods
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This document describes a coin recognition system developed using MATLAB digital image processing techniques. It discusses two methods: a static image method where a single image is converted to binary, filled, and analyzed using edge detection and circle finding algorithms to identify coins. A video streaming method takes periodic screenshots, converts them, and compares measurements to a stored database to identify coins in real-time video. Key steps include image conversion, feature extraction using edge detection and circle finding, and measurement comparison to a stored database of coin images. The system is designed to maintain a consistent distance and angle between input images and the database images for accurate recognition.
Fidelity criteria in image compression
This document discusses fidelity criteria in image compression. It defines fidelity as the degree of exactness of reproduction and identifies two types of fidelity criteria: objective and subjective. Objective criteria measure information loss mathematically between original and compressed images, using metrics like root mean square error and peak signal-to-noise ratio. Subjective criteria involve human evaluations of compressed image quality based on rating scales. The document also describes the basic components of image compression systems, including encoders, decoders, mappers, quantizers and symbol coders.
Color to Gray and back’ using normalization of color components with Cosine, ...
This document proposes three methods for converting a color image to grayscale while embedding color information, and then recovering the original color image from the grayscale version. The first method embeds normalized color components in the LH and HL subbands of the wavelet transform. The second method embeds them in the HL and HH subbands. The third method embeds in the LH and HH subbands. Experimental results show that the second method performs better than the first and third methods for color to grayscale conversion and recovery across different wavelet transforms. The goal is to reduce image size by a factor of three while retaining the ability to recover the original color image when needed.
Introduction to Digital Image Processing Using MATLAB
This was a 3 hour presentation given to undergraduate and graduate students at Ryerson University in Toronto, Ontario, Canada on an introduction to Digital Image Processing using the MATLAB programming environment. This should provide the basics of performing the most common image processing tasks, as well as providing an introduction to how digital images work and how they're formed.
You can access the images and code that I created and used here: https://www.dropbox.com/sh/s7trtj4xngy3cpq/AAAoAK7Lf-aDRCDFOzYQW64ka?dl=0
Image compression
A description about image Compression. What are types of redundancies, which are there in images. Two classes compression techniques. Four different lossless image compression techiques with proper diagrams(Huffman, Lempel Ziv, Run Length coding, Arithmetic coding).
Sunum
This document compares novel boundary detection methods. It summarizes Sketch Tokens, which learns mid-level representations for contour detection using random forests on clustered edge patches. Crisp Boundary Detection uses pointwise mutual information between pixel colors to determine boundaries. Structured Forests directly learn edge structures without pre-clustering, while Oriented Edge Forest recognizes oriented edge patterns in a scanning window. Experiments on BSDS images show Sketch Tokens and Crisp Boundary Detection have simpler models but lower performance than Structured Forests, while Oriented Edge Forest has better speed but lower accuracy.
Ec section
This document discusses digital image processing using MATLAB. It begins by defining digital images and how they are represented by arrays of pixels in computer memory. It then discusses how images can be read into MATLAB and converted between color, grayscale, and binary representations. Various image processing operations are described such as edge detection, dilation, filling, and calculating region properties. Finally, examples are given of processing color images using intensity transformations and gamma correction.
Transform coding
Transform coding is a lossy compression technique that converts data like images and videos into an alternate form that is more convenient for compression purposes. It does this through a transformation process followed by coding. The transformation removes redundancy from the data by converting pixels into coefficients, lowering the number of bits needed to store them. For example, an array of 4 pixels requiring 32 bits to store originally might only need 20 bits after transformation. Transform coding is generally used for natural data like audio and images, removes redundancy, lowers bandwidth, and can form images with fewer colors. JPEG is an example of transform coding.
Ba34321326
This document discusses a proposed method for color image authentication using digital image watermarking and histograms. It begins with an abstract describing the goal of robust digital color image watermarking for authentication while minimizing embedding distortion. It then provides background on digital watermarking techniques and reviews some previous related works. The proposed method embeds a watermark into the least significant bit of each color plane (red, green, blue) of the cover image. It describes the insertion and extraction algorithms in detail. Experimental results on test images are provided, analyzing the peak signal-to-noise ratio for each color plane between the original and watermarked images. The method is concluded to enhance visibility and robustness of the watermark for image authentication.
Dictionary based Image Compression via Sparse Representation
Nowadays image compression has become a necessity due to a large volume of images. For efficient use of storage space and data transmission, it becomes essential to compress the image. In this paper, we propose a dictionary based image compression framework via sparse representation, with the construction of a trained over-complete dictionary. The overcomplete dictionary is trained using the intra-prediction residuals obtained from different images and is applied for sparse representation. In this method, the current image block is first predicted from its spatially neighboring blocks, and then the prediction residuals are encoded via sparse representation. Sparse approximation algorithm and the trained overcomplete dictionary are applied for sparse representation of prediction residuals. The detail coefficients obtained from sparse representation are used for encoding. Experimental result shows that the proposed method yields both improved coding efficiency and image quality as compared to some state-of-the-art image compression methods.
I07015261
This document discusses fractal image compression based on jointly and different partitioning schemes. It proposes partitioning RGB images into range blocks in two ways: 1) Jointly, where the red, green, and blue channels are partitioned together into blocks of the same size and coordinates. 2) Differently, where each channel is partitioned independently, resulting in different block sizes and coordinates for each channel. The document provides background on fractal image compression and the encoding/decoding processes. It analyzes the two partitioning schemes and argues the different scheme is more effective for encoding by allowing each channel to have customized partitioning.
Image processing with matlab
This document discusses image processing with MATLAB. It provides an overview of the different image formats supported by MATLAB, including JPEG, TIFF, and BMP. It also describes the different types of images like binary, grayscale, and RGB images. It explains how to read images into MATLAB, extract color channels, remove noise, and find properties like the centroid and area. Finally, it discusses how to do serial communication between MATLAB and an Arduino board to process images in real-time and send signals to a microcontroller.
Image compression
The document discusses digital image compression. It describes how image compression works by removing redundant data from images to reduce file sizes. It also discusses various image file formats and compression standards like JPEG and MPEG that are commonly used to compress images and video. Finally, it explains several lossy and lossless compression methods and algorithms, such as Huffman coding and Golomb coding, that form the technical basis for these compression standards.
Image Processing Using MATLAB
The document discusses a workshop on image processing using MATLAB. It provides an overview of MATLAB and its image processing toolbox. It describes how to read, display, and convert between different image formats in MATLAB. It also demonstrates various image processing operations that can be performed, such as arithmetic operations, conversion between color and grayscale, image rotation, blurring and deblurring, and filling regions of interest. The document aims to introduce the basics of working with images in the MATLAB environment.
Fundamentals and image compression models
This document discusses digital image compression. It notes that compression is needed due to the huge amounts of digital data. The goals of compression are to reduce data size by removing redundant data and transforming the data prior to storage and transmission. Compression can be lossy or lossless. There are three main types of redundancy in digital images - coding, interpixel, and psychovisual - that compression aims to reduce. Channel encoding can also be used to add controlled redundancy to protect the source encoded data when transmitted over noisy channels. Common compression methods exploit these different types of redundancies.
Image compression
The document discusses image compression techniques. It explains that the goal of image compression is to reduce irrelevant and redundant image data to store and transmit images more efficiently. There are three main types of redundancy reduced in image compression: coding, interpixel, and psychovisual. Lossless compression preserves all image data using techniques like Huffman coding, run-length coding, arithmetic coding, and Lempel-Ziv coding. Lossy compression allows for some quality loss and higher compression ratios.
IRJET-Lossless Image compression and decompression using Huffman coding
S.Anitha"Lossless image compression and decompression using huffman coding", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net
Abstract
This paper propose a novel Image compression based on the Huffman encoding and decoding technique. Image files contain some redundant and inappropriate information. Image compression addresses the problem of reducing the amount of data required to represent an image. Huffman encoding and decoding is very easy to implement and it reduce the complexity of memory. Major goal of this paper is to provide practical ways of exploring Huffman coding technique using MATLAB .
Image proceesing with matlab
This document provides an overview and examples of using MATLAB. It introduces MATLAB, describing its origins and applications in fields like aerospace, robotics, and more. It then covers various topics within MATLAB like image processing, reading and writing images, converting images to binary and grayscales, plotting functions, and using GUI tools. Examples of code are provided for tasks like reading images, filtering noise, and capturing video from a webcam. The document also lists some common file extensions used in MATLAB and describes serial communication.
Bca5042– graphics and multimedia
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
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 ...
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www.ijerd.com
This document discusses using density plots generated in Mathematica to create innovative graphic designs suitable for the textile industry. Density plots arrange shades or colors in a rectangular mesh, making them well-suited for implementation on looms. The author explores using mathematical functions to generate designs with unusual 3, 5, or 7-fold symmetries instead of the typical 4, 6, or 8-fold patterns. Examples of designs include those based on complex number roots that exhibit different symmetrical patterns. Fractal functions are also investigated as a source for unique textile patterns.
Introductory Digital Image Processing using Matlab, IIT Roorkee
Introduction to Digital Image Processing using Matlab, IIT Roorkee. Organized by Electronics Section, Hobbies Club.
Reference: Gonzalez and Woods
Coin recognition using matlab
This document describes a coin recognition system developed using MATLAB digital image processing techniques. It discusses two methods: a static image method where a single image is converted to binary, filled, and analyzed using edge detection and circle finding algorithms to identify coins. A video streaming method takes periodic screenshots, converts them, and compares measurements to a stored database to identify coins in real-time video. Key steps include image conversion, feature extraction using edge detection and circle finding, and measurement comparison to a stored database of coin images. The system is designed to maintain a consistent distance and angle between input images and the database images for accurate recognition.
Fidelity criteria in image compression
This document discusses fidelity criteria in image compression. It defines fidelity as the degree of exactness of reproduction and identifies two types of fidelity criteria: objective and subjective. Objective criteria measure information loss mathematically between original and compressed images, using metrics like root mean square error and peak signal-to-noise ratio. Subjective criteria involve human evaluations of compressed image quality based on rating scales. The document also describes the basic components of image compression systems, including encoders, decoders, mappers, quantizers and symbol coders.
Color to Gray and back’ using normalization of color components with Cosine, ...
This document proposes three methods for converting a color image to grayscale while embedding color information, and then recovering the original color image from the grayscale version. The first method embeds normalized color components in the LH and HL subbands of the wavelet transform. The second method embeds them in the HL and HH subbands. The third method embeds in the LH and HH subbands. Experimental results show that the second method performs better than the first and third methods for color to grayscale conversion and recovery across different wavelet transforms. The goal is to reduce image size by a factor of three while retaining the ability to recover the original color image when needed.
Introduction to Digital Image Processing Using MATLAB
This was a 3 hour presentation given to undergraduate and graduate students at Ryerson University in Toronto, Ontario, Canada on an introduction to Digital Image Processing using the MATLAB programming environment. This should provide the basics of performing the most common image processing tasks, as well as providing an introduction to how digital images work and how they're formed.
You can access the images and code that I created and used here: https://www.dropbox.com/sh/s7trtj4xngy3cpq/AAAoAK7Lf-aDRCDFOzYQW64ka?dl=0
Image compression
A description about image Compression. What are types of redundancies, which are there in images. Two classes compression techniques. Four different lossless image compression techiques with proper diagrams(Huffman, Lempel Ziv, Run Length coding, Arithmetic coding).
Sunum
This document compares novel boundary detection methods. It summarizes Sketch Tokens, which learns mid-level representations for contour detection using random forests on clustered edge patches. Crisp Boundary Detection uses pointwise mutual information between pixel colors to determine boundaries. Structured Forests directly learn edge structures without pre-clustering, while Oriented Edge Forest recognizes oriented edge patterns in a scanning window. Experiments on BSDS images show Sketch Tokens and Crisp Boundary Detection have simpler models but lower performance than Structured Forests, while Oriented Edge Forest has better speed but lower accuracy.
Ec section
This document discusses digital image processing using MATLAB. It begins by defining digital images and how they are represented by arrays of pixels in computer memory. It then discusses how images can be read into MATLAB and converted between color, grayscale, and binary representations. Various image processing operations are described such as edge detection, dilation, filling, and calculating region properties. Finally, examples are given of processing color images using intensity transformations and gamma correction.
Transform coding
Transform coding is a lossy compression technique that converts data like images and videos into an alternate form that is more convenient for compression purposes. It does this through a transformation process followed by coding. The transformation removes redundancy from the data by converting pixels into coefficients, lowering the number of bits needed to store them. For example, an array of 4 pixels requiring 32 bits to store originally might only need 20 bits after transformation. Transform coding is generally used for natural data like audio and images, removes redundancy, lowers bandwidth, and can form images with fewer colors. JPEG is an example of transform coding.
Ba34321326
This document discusses a proposed method for color image authentication using digital image watermarking and histograms. It begins with an abstract describing the goal of robust digital color image watermarking for authentication while minimizing embedding distortion. It then provides background on digital watermarking techniques and reviews some previous related works. The proposed method embeds a watermark into the least significant bit of each color plane (red, green, blue) of the cover image. It describes the insertion and extraction algorithms in detail. Experimental results on test images are provided, analyzing the peak signal-to-noise ratio for each color plane between the original and watermarked images. The method is concluded to enhance visibility and robustness of the watermark for image authentication.
Dictionary based Image Compression via Sparse Representation
Nowadays image compression has become a necessity due to a large volume of images. For efficient use of storage space and data transmission, it becomes essential to compress the image. In this paper, we propose a dictionary based image compression framework via sparse representation, with the construction of a trained over-complete dictionary. The overcomplete dictionary is trained using the intra-prediction residuals obtained from different images and is applied for sparse representation. In this method, the current image block is first predicted from its spatially neighboring blocks, and then the prediction residuals are encoded via sparse representation. Sparse approximation algorithm and the trained overcomplete dictionary are applied for sparse representation of prediction residuals. The detail coefficients obtained from sparse representation are used for encoding. Experimental result shows that the proposed method yields both improved coding efficiency and image quality as compared to some state-of-the-art image compression methods.
I07015261
This document discusses fractal image compression based on jointly and different partitioning schemes. It proposes partitioning RGB images into range blocks in two ways: 1) Jointly, where the red, green, and blue channels are partitioned together into blocks of the same size and coordinates. 2) Differently, where each channel is partitioned independently, resulting in different block sizes and coordinates for each channel. The document provides background on fractal image compression and the encoding/decoding processes. It analyzes the two partitioning schemes and argues the different scheme is more effective for encoding by allowing each channel to have customized partitioning.
Image processing with matlab
This document discusses image processing with MATLAB. It provides an overview of the different image formats supported by MATLAB, including JPEG, TIFF, and BMP. It also describes the different types of images like binary, grayscale, and RGB images. It explains how to read images into MATLAB, extract color channels, remove noise, and find properties like the centroid and area. Finally, it discusses how to do serial communication between MATLAB and an Arduino board to process images in real-time and send signals to a microcontroller.
Image compression
The document discusses digital image compression. It describes how image compression works by removing redundant data from images to reduce file sizes. It also discusses various image file formats and compression standards like JPEG and MPEG that are commonly used to compress images and video. Finally, it explains several lossy and lossless compression methods and algorithms, such as Huffman coding and Golomb coding, that form the technical basis for these compression standards.
Image Processing Using MATLAB
The document discusses a workshop on image processing using MATLAB. It provides an overview of MATLAB and its image processing toolbox. It describes how to read, display, and convert between different image formats in MATLAB. It also demonstrates various image processing operations that can be performed, such as arithmetic operations, conversion between color and grayscale, image rotation, blurring and deblurring, and filling regions of interest. The document aims to introduce the basics of working with images in the MATLAB environment.
Fundamentals and image compression models
This document discusses digital image compression. It notes that compression is needed due to the huge amounts of digital data. The goals of compression are to reduce data size by removing redundant data and transforming the data prior to storage and transmission. Compression can be lossy or lossless. There are three main types of redundancy in digital images - coding, interpixel, and psychovisual - that compression aims to reduce. Channel encoding can also be used to add controlled redundancy to protect the source encoded data when transmitted over noisy channels. Common compression methods exploit these different types of redundancies.
Image compression
The document discusses image compression techniques. It explains that the goal of image compression is to reduce irrelevant and redundant image data to store and transmit images more efficiently. There are three main types of redundancy reduced in image compression: coding, interpixel, and psychovisual. Lossless compression preserves all image data using techniques like Huffman coding, run-length coding, arithmetic coding, and Lempel-Ziv coding. Lossy compression allows for some quality loss and higher compression ratios.
IRJET-Lossless Image compression and decompression using Huffman coding
S.Anitha"Lossless image compression and decompression using huffman coding", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net
Abstract
This paper propose a novel Image compression based on the Huffman encoding and decoding technique. Image files contain some redundant and inappropriate information. Image compression addresses the problem of reducing the amount of data required to represent an image. Huffman encoding and decoding is very easy to implement and it reduce the complexity of memory. Major goal of this paper is to provide practical ways of exploring Huffman coding technique using MATLAB .
Image proceesing with matlab
This document provides an overview and examples of using MATLAB. It introduces MATLAB, describing its origins and applications in fields like aerospace, robotics, and more. It then covers various topics within MATLAB like image processing, reading and writing images, converting images to binary and grayscales, plotting functions, and using GUI tools. Examples of code are provided for tasks like reading images, filtering noise, and capturing video from a webcam. The document also lists some common file extensions used in MATLAB and describes serial communication.
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Similar to Bt9301 computer graphics (1)
Bca5042– graphics and multimedia
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
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 ...
Bt9301, computer graphics
This document advertises assignment help services for students. It provides contact information for students to send their semester details and specialization to get fully solved assignments via email or phone call. It also includes sample questions from an assignment in Computer Graphics for BSc IT Sixth Semester, covering topics like video display controllers, midpoint circle drawing, polygon filling algorithms, line clipping, projections, and 3D transformations. Students are encouraged to contact the provided email address or phone number to receive solved assignments.
Semester ii
This document provides information about assignments for various courses in the Post Graduate Diploma in Computer Applications (PGDCA) program. It lists the assignment submission deadlines for different courses, which are October 31, 2020 for the July 2020 session and April 15, 2021 for the January 2021 session. It provides details of the assignment questions and guidelines for submitting assignments. Students are advised to submit their assignments to their study center coordinator before the due date and attend the viva voce for assignments.
Implementation of Picwords to Warping Pictures and Keywords through Calligram
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Computer graphics notes
This document provides an overview of computer graphics and its applications. It discusses various types of video display devices used in computer graphics like CRTs and flat panel displays. It describes how raster scan and random scan systems work and lists common input and output devices. The document outlines different chapters that will cover topics like line and curve generation algorithms, transformations, 3D viewing, surface detection, and modeling techniques. It provides examples of how computer graphics is used in fields like CAD, presentations, entertainment, education, visualization, image processing, and graphical user interfaces.
Computer graphics 9068
This document provides information about a 4th semester computer engineering course on computer graphics. The course code is CO/CM/CD 9068. It includes 3 hours of theory and 2 hours of practical per week. Assessment includes an end of semester exam worth 80 marks, a theory test worth 20 marks, and an oral exam worth 25 marks. The rationale explains how computer graphics is used to convey information visually and its applications. The objectives are to learn algorithms for drawing lines, circles, polygons and natural objects as well as transformations, raster graphics, and interactive graphics. The content will cover basics, shapes, transformations, windowing, curves, fractals, and interactive graphics. Practical sessions will develop programming skills and include implementing various computer
CG_Unit1_SShah.pptx
Introduction, graphics primitives :Pixel, resolution, aspect ratio, a frame buffer. Display devices, and applications of computer graphics.
Scan conversion - Line drawing algorithms: Digital Differential Analyzer (DDA), Bresenham’s Circle drawing algorithms: DDA, Bresenham’s, and Midpoint.
Presentation vision transformersppt.pptx
The document describes a paper that explores using transformer architectures for computer vision tasks like image recognition. The authors tested various vision transformer (ViT) models on datasets like ImageNet and CIFAR-10/100. Their ViT models divided images into patches, embedded them, and fed them into a transformer encoder. Larger ViT models performed better with more training data. Hybrid models that used ResNet features before the transformer worked better on smaller datasets. The authors' results showed ViT models can match or beat CNNs like ResNet for image recognition, especially with more data.
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.
Computer Graphics Notes
The document provides an overview of an introductory computer graphics course. It outlines the course objectives of understanding fundamental graphical operations, recent advances in computer graphics, and user interface issues. It then lists and briefly describes the main topics that will be covered in the course, including basic raster graphics, 2D transformations, clipping, filling techniques, 3D graphics, visibility, and advanced topics like rendering, raytracing, antialiasing and fractals.
Graphics Standards and Algorithm
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An improved image compression algorithm based on daubechies wavelets with ar...
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Computer Graphics Practical
The document discusses computer graphics and provides examples of its applications. It discusses graphics inbuilt functions such as arc(), initgraph(), closegraph(), and line(). It provides code snippets and explanations for these functions. It also lists algorithms for direct and Bresenham lines, circles, ellipses, and their code programs. Finally, it mentions static and dynamic applications of computer graphics.
Survey paper on image compression techniques
This document summarizes and compares several popular image compression techniques: wavelet compression, JPEG/DCT compression, vector quantization (VQ), fractal compression, and genetic algorithm compression. It finds that all techniques perform satisfactorily at 0.5 bits per pixel, but for very low bit rates like 0.25 bpp, wavelet compression techniques like EZW perform best in terms of compression ratio and quality. Specifically, EZW and JPEG are more practical than others at low bit rates. The document also notes advantages and disadvantages of each technique and concludes hybrid approaches may achieve even higher compression ratios while maintaining image quality.
Using A Application For A Desktop Application
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This case study examines the impact of sales, fixed assets, and interest paid on the profitability of a major logistics company, GATI Limited, using multiple linear regression analysis. The regression analysis found that profitability is significantly and positively impacted by increases in fixed assets, and significantly and negatively impacted by increases in interest paid. Sales volume has a positive but minimal impact on profitability. Seasonality was also found to impact profitability. Overall, infrastructure development programs are expected to strengthen growth for the logistics industry by reducing costs, though current economic conditions remain challenging due to global slowdown.
Day 1 presentation terminology
This document provides an overview of digital media concepts including resolution, frame rates, software, file types, 3D modeling terminology, and learning outcomes. It discusses standard definition and high definition video resolutions and frame rates. 3D and 2D software are listed for modeling, animation, compositing, and tracking. File types for images, video, and vectors are also covered. Basic 3D modeling concepts such as polygons, vertices, edges, faces, and normals are defined. NURBS modeling is introduced. The document outlines assignments for a 3D animation project involving designing and animating a domino chain reaction scene. Deliverables include reference drawings, a photomontage, vector illustration, texture library, and final animation visual
MCA-ASS-Semester V.pdf
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More recently, we’ve seen gains from AI for combinatorial optimization, leading to scalability of the method, as well as significant reductions in cost. This method replaces the manual tuning of traditional heuristic approaches with an AI agent that provides a fast metric estimation.
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Bt9301 computer graphics (1)
- 1. Dear students get fully solved assignments Send your semester & Specialization name to our mail id : “ help.mbaassignments@gmail.com ” or Call us at : 08263069601 (Prefer mailing. Call in emergency ) ASSIGNMENT DRIVE WINTER 2016 PROGRAM BSc IT SEMESTER SIXTH SUBJECT CODE & NAME BT9301 - Computer Graphics BK ID B0810 CREDITS 4 MARKS 60 Note: Answer all questions. Kindly note that answers for 10 marks questions should be approximately of 400 words. Each question is followed by evaluation scheme. Question. 1. What are the benefits of interactive graphics? Answer:Today, a high quality graphics displays of personal computer provide one of the most natural means of communicating with a computer It providestoolsforproducingpictures notonlyof concrete,‘real-world’objectsbutalsoof abstract, syntheticobjects,suchasmathematical surfacesin 4D and of data that have no inherent geometry, such as survey results. Question. 2. Explain Raster Display System with Peripheral Display Processor. Answer:Incomputergraphics, a raster graphics image is a dot matrix data structure, representing a generallyrectangulargridof pixels,orpointsof color,viewableviaamonitor,paper,orotherdisplay medium. Raster images are stored in image files with varying formats. A bitmap,a single-bitraster,correspondsbit-for-bit with an image displayed on a screen, generally in the same format used for storage in the Question. 3. Explain the process of Video Mixing.
- 2. Answer:The term video editing can refer to: The process of manipulating video images. Once the province of expensive machines called video editors, video editing software is now available for personal computers and workstations. Video editing includes cutting segments (trimming), re- sequencing clips, and adding transitions and other Special Effects. Linearvideoediting,usingvideo tape and is edited in a very linear way. Several video clips from different tapes are recorded to one single tape in the order that they will appear. Non-linear editing system (NLE), This is Question. 4. Mention the basic Concepts in Line Drawing. Answer:A line drawing algorithm is a graphical algorithm for approximating a line segment on discrete graphical media.Ondiscrete media,suchaspixel-based displays and printers, line drawing requiressuchanapproximation(innontrivial cases). Basic algorithms rasterize lines in one color. A better representation with multiple color gradations requires an advanced process, spatial anti - aliasing. Question. 5. Explain Two dimensional transformations. Answer:The two-dimensionaltransformationsof scaling, rotation and translation can be expressed inan algebraicformulationorina matrix formulation.The algebraicformulationisthe besttofollow in implementation but the matrix formulation has importance at a conceptual level. 2D graphicsmodelsmaycombine geometricmodels(alsocalledvectorgraphics),digital images(also calledrastergraphics),texttobe typeset(definedbycontent,fontstyle andsize,color,position,and orientation), mathematical functions and equations, and Question. 6. Explain Incremental Algorithm for line drawing. Answer:Bresenham's linealgorithmisanalgorithmthat determines the points of an n-dimensional raster thatshouldbe selectedinordertoforma close approximationtoa straight line between two points. It is commonly used to draw line primitives in a Dear students get fully solved assignments Send your semester & Specialization name to our mail id : “ help.mbaassignments@gmail.com ” or Call us at : 08263069601
- 3. (Prefer mailing. Call in emergency )