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.
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
Color fundamentals and color models - Digital Image ProcessingAmna
This presentation is based on Color fundamentals and Color models.
~ Introduction to Colors
~ Color in Image Processing
~ Color Fundamentals
~ Color Models
~ RGB Model
~ CMY Model
~ CMYK Model
~ HSI Model
~ HSI and RGB
~ RGB To HSI
~ HSI To RGB
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
Color fundamentals and color models - Digital Image ProcessingAmna
This presentation is based on Color fundamentals and Color models.
~ Introduction to Colors
~ Color in Image Processing
~ Color Fundamentals
~ Color Models
~ RGB Model
~ CMY Model
~ CMYK Model
~ HSI Model
~ HSI and RGB
~ RGB To HSI
~ HSI To RGB
Image Restoration And Reconstruction
Mean Filters
Order-Statistic Filters
Spatial Filtering: Mean Filters
Adaptive Filters
Adaptive Mean Filters
Adaptive Median Filters
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
Digital Image Processing denotes the process of digital images with the use of digital computer. Digital images are contains various types of noises which are reduces the quality of images. Noises can be removed by various enhancement techniques. Image smoothing is a key technology of image enhancement, which can remove noise in images.
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
Image Restoration And Reconstruction
Mean Filters
Order-Statistic Filters
Spatial Filtering: Mean Filters
Adaptive Filters
Adaptive Mean Filters
Adaptive Median Filters
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
Digital Image Processing denotes the process of digital images with the use of digital computer. Digital images are contains various types of noises which are reduces the quality of images. Noises can be removed by various enhancement techniques. Image smoothing is a key technology of image enhancement, which can remove noise in images.
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
COM2304: Intensity Transformation and Spatial Filtering – II Spatial Filterin...Hemantha Kulathilake
At the end of this lecture, you should be able to;
describe the fundamentals of spatial filtering.
generating spatial filter masks.
identify smoothing via linear filters and non linear filters.
apply smoothing techniques for problem solving.
When Discrete Optimization Meets Multimedia Security (and Beyond)Shujun Li
Invited talk at the FoT-RSS: Faculty of Technology Research Seminar Series, De Montfort University, UK, co-sponsored by the IEEE UK & Ireland Signal Processing Chapter, 25 May 2016
Abstract:
Selective encryption has been widely used for image and video encryption due to many practical reasons such as to achieve format compliance and perceptual encryption, to avoid negative impact on compression efficiency, and to make the multimedia processing pipeline more modular and thus reconfigurable. The seminar will present research on modelling recovery of missing information with different structures in digital images as a discrete optimization problem. In the context of selective encryption, the structure of missing information is defined by the underlying selective encryption algorithm, where the selectively encrypted information is considered missing from an attacker's point of view. Experimental results showed that the new approach can significantly improve the performance of error-concealment attacks compared to the state of the art in terms of visual quality of the recovered images. The approach can be applied to other areas of multimedia security and multimedia processing in general where the structure of missing information in digital signals is known. An example of adapting the model to self-recovery image authentication watermarking will be shown.
Fundamental concepts and basic techniques of digital image processing. Algorithms and recent research in image transformation, enhancement, restoration, encoding and description. Fundamentals and basic techniques of pattern recognition.
At the end of this lesson, you should be able to;
describe spatial resolution
describe intensity resolution
identify the effect of aliasing
describe image interpolation
describe relationships among the pixels
Image Quality Feature Based Detection Algorithm for Forgery in Images ijcga
The verifying of authenticity and integrity of images is a serious research issue. There are various types of techniques to create forged images for various intentions. In this paper, Attempt is made to verify the authenticity of image using the image quality features like markov and moment based features. They are found to have their best results in case of forgery involving splicing.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
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We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
1. “ Fidelity Criteria In Image
Compression”
• Mr. Kadam Pawan Prakash
M.Sc.Electronics
2. Topics :
• What is fidelity?
• Types of Fidelity criteria
• Image Compression Systems
3. Fidelity Criteria :-
• Fidelity: The degree of exactness with which something is
copied or reproduced called Fidelity.
• To determine exactly what information is important ,and
able to measure image quality, we need to define image
fidelity criteria.
4. Fidelity Criteria Cont…:-
• Fidelity can be divided into two types;
• 1. Objective Fidelity
• 2. Subjective Fidelity
5. 1.Objective fidelity Criteria:
• When the level of information loss can be expressed as a
function of the original or input image and the compressed
and subsequently decompressed output image is said to be
based on an Objective fidelity criteria.
6. Objective fidelity Criteria Cont…
),( yxf
),(ˆ yxf
Let denote an estimate approximation of that results
from compressing and subsequently decompressing the input. For
any value of x and y, the error e(x,y) between and can
be defined as;
e(x,y)= -
),(ˆ yxf),( yxf
),( yxf
),(ˆ yxf
So that the total error between the two images is,
]),(),(ˆ[
1
0
1
0
∑∑
−
=
−
=
−
M
x
N
y
yxfyxf
7. Objective fidelity Criteria cont…
• Where the images are of size M N.
• The root mean square between and then
is the square root of the squared error averaged over the
M N array or;
×
),( yxf ),(ˆ yxf
×
2
)],(),([
1
yxfyxf
MN
erms −=
8. Objective fidelity Criteria cont…
• A closely related objective fidelity criteria is the mean-
signal to noise ratio of compressed and decompressed
image. If is considered to be the sum of the
original image and a noise signal e ,the
mean-square signal to-noise ratio of the output image
,denoted by SNRrms is ;
( )[ ]
( ) ( )[ ]∑∑
∑∑
−
=
−
=
−
=
−
=
−
= 1
0
1
0
2
1
0
1
0
2
,,ˆ
,ˆ
M
x
N
y
M
x
N
y
rms
yxfyxf
yxf
SNR
( )yxf ,ˆ
( )yxf , ( )yx,
The rms value of the signal is-to-noise ratio denoted by
SNRms is obtained by taking square root of given equation.
9. 2.Subjective Criteria :
• Measuring image quality by the subjective evaluations of a
human observer is often more appropriate since most
decompressed images are ultimately viewed by human
beings. This can be accomplished by showing a
decompressed image to a viewers and averaging their
evaluations. An example of a rating scale is shown in the
following table. The evaluations are said to be based on
subjective fidelity criteria.
10.
11. Image Compression Systems :
• A compression system consists of two distinct
stuctural blocks: an encoder and a decoder. An
input image is fed into the encoder, which
creates a set of symbols from the input data. After
transmission over the channel, the encoded
representation is fed to the decoder, where a
reconstructed output image is generated. In
general, may or may not be an exact replica
of .
),( yxf
),( yxf
),(ˆ yxf
),(ˆ yxf
12. Image Compression Systems cont… :
The encoder is made up of :
• a source encoder, which removes input redundancies,
and
• a channel encoder, which increases the noise immunity
of the source encoder’s output.
The decoder includes
• a channel decoder followed by
• a source decoder.
13. If the channel between the encoder and decoder is
noise free, the channel encoder and decoder are omitted.
14. Source Encoder and Decoder :
The source encoder is responsible for reducing or
eliminating any coding, interpixel, or psychovisual
redundancies in the input image. The approach can be
modelled by a series of three independent operations.
(a) Mapper:
This transforms the input data into a format
designed to reduce interpixel redundancies in the input
image. This operation generally is reversible and may or
may not reduce directly the amount of data required to
represent the image. Examples of such operations are run-
length coding and transform coding.
15. (b) Quantizer:
This reduces the accuracy of the mapper’s
output in accordance with some pre-established fidelity
criterion. This stage reduces the psychovisual
redundancies of the input image. The operation is
irreversible and must be omitted when error-free
compression is desired.
(c) Symbol coder:
This stage creates a fixed- or variable-
length code to represent the quantizer output.
16. Source Decoder:
The source decoder contains only two components: a
symbol decoder and an inverse mapper. Because
quantization results in irreversible information loss, an
inverse quantizer block is not included in the general
source decoder model.
17. The Channel Encoder and Decoder :
The channel encoder and decoder are designed to
reduce the impact of channel noise by inserting a
controlled form of redundancy into the source encoded
data. One of the most useful channel encoding techniques
is the Hamming code.
18. Reference :
• Digital Image Processing by Rafafel C.Gonzalez.
Source : Dept. Library
• Computer Imaging (Digital Image Analysis And
Processing) By Scott E Umbaugh
Source : google book