Very curiosity on the presented topic of Image compression by my fellow citizen students of Tumaini University.
Ms Zuena Mgova
Mr. Justine L. Ngimba &
By 20th/april/2009, the presented topic is well comprehended. In Image compression I have comes to recognized that is good and very applicable in most of industrial countries especially in most Photoshop’s. Picture managements and processing so as to reduce the content size of images / pictures.
Meaning.
Image compression is reducing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The lessening in file size allows more images to be stored in a given amount of disk or memory space.
Types of image compression.
(a)Lossless compressionImage compression as the Lossless density of image from time to time can be applicable in most of the technical portrayal for drawing of image and reduction of its size after being scanned, icon or cartoon strips are also reduced their size in most of the digital pictures and videos. This is suitable for artificial images of high value content, such as medical imagery or image scans made for archival purposes
(b)Lossy compression
Is to lower the resolution of an image, as in image scaling, particularly decimation and
Method of one, where compressing data and then decompressing it retrieves data that may well be different from the original, but is close enough to be useful in some way.
Commonly used to compress multimedia data (audio, video, still images), especially in applications such as streaming media and internet telephony
IMAGE FORMAT.
There several types of image format which mostly used includes:-GIF, this is superior in image quality; file size does significantly better on images with only a few distinct colors,
Such as line drawings and simple cartoons but stands for Graphical Interchange Format.PNG specification explains its basic rationale thus: The PNG format provides a portable, legally unencumbered, well-compressed, well-specified standard of image and stands for Portable network graphics
(i)BMP (Bitmap) - is the native bitmap file format of the Microsoft Windows environment.It efficiently stores mapped or unmapped RGB graphics data with pixels,
(ii)BMP is an excellent choice for a simple bitmap format which supports a wide range of RGB image data in which can be stored raw or compressed using a4-bit or 8-bit RLE data compression algorithm
(iii)TIFF (Tagged Image File Format) - The TIFF format was formerly owned and maintained by the Aldus Developer'sAssociation. Aldus has since merged with Adobe Systems and now the Adobe Developers Association (ADA) maintains the TIFF file format. You may obtain aCopy of the TIFF specification in most of PDF formats.
(iv)JPEG, this is a standardized image compression mechanism which stands for Joint Photographic Experts Group.
ADVANTAGES OF IMAGE COMPRESSION.
(i)Condense the data storage necessities
-In reduction of size helps us about more space during data storage and other information which replaces most of the storage legroom required by the whole image.
(ii)Compact the time of images during downloading and uploading process.
-It is true that compression of image contents but remaining of good resolutions is possible to take a little amount time during download and upload of image formats, such as in E-mail attachment, Web pages, Photo sharing websites ctc.
DISADVANTAGES OF IMAGE COMPRESSION.
(i)Interference of data properties
-The process of image decrease always disturbs the equilibrium of image properties as the results can lead to look in bad resolution pictures.
(ii)rim down reliability of image records
-This lie down in most of image information where people are able to keep on records of reliable or quality image compression.
(iii)Time consuming.
-The process of compressing image take a long process as in consuming a lot of time being used in other productive activities lather than in image compressing
(iv)Dropdown of in sequence or bits.
-This faced in most of digital devices where most of image is countable by the number of bit as converted in pixels and percents of the image size.
CHALLENGES OF IMAGE COMPRESSION
(i)Bad image / picture resolution
(ii)Insuffient funds for controlling the process
(iii)Poor knowledge of reducing the amount size of image
HOW TO OVERCOME / MINIMIZE THE CHALLENGES
Firstly, we have to know that in compressing an image, start from the original image since compression from compressed image leads to poor image quality, so we are supposed not to save to the new format if we think that we need to still proceed in compressing and image.
Secondly, In order to compress the amount size of image we have to get at least a little knowledge on how to do as well as enough funds for buying the facilities like computer, scanners, Cameras (Digitals).
TERMINATION,
Although one main goal of digital audio perceptual coders is data reduction, this is not a necessary characteristic. As we know, perceptual coding can be used to improve the representation of digital audio through advanced bit allocation as image compression concerned.
Refference
(i)Koff, D., Shulman, Harry, An Overview of Digital Compression of Medical Images: Can We Use Lossy Image Compression in Radiology?, CARJ vol 57, No 4, October 2006
(ii)Brislawn, C. M., and Quirk M.D. Image compression with the JPEG-2000 standard. In: Encyclopedia of Optical Engineering. Driggers, R.D., eds, New York: Marcel Dekker; 2003 pp 780-785.
(iii)JPEG2000 Lossless and Lossy Compression of Continuous-Tone and Bi-level Still Images," 1999.
(iv)"Information Technology—Digital Compression and Coding of Continuous-Tone Still Images.“
(v)http://www.tangotools.com/jpegsizer/features.htm
(vi)http://www.scartips.com
(vi)http://www.htmlgoodies.com/tutorials
By Venance
25 Apr 2009
DATA MINING
LEARNING DIARY ON DATA MINING.
Am very interested with the presentation topic of Data Mining which was accessible by
Mr. Joshua Shendu &
Remy Kaaro
On 17/04/2009 there was the occurrence of the presented topic which was the best and well comprehended. This topic is less smooth but I came to appreciate that is excellent and well due to the fact that the presenters were prepared to do their task!
THE MEARNING OF DATA MINING. Data mining.
- Data Mining (also known as Knowledge Discovery) technology helps businesses discover hidden data patterns and provides predictive information which can be applied to benefit the business
Or
-As the process of investigate data from different point of view and shortening it into useful information that can be used to increase income, cuts expenses, or both. Or is the process of finding correlations or patterns among dozens of fields in large relational databases.
Data mining software is analytical tool used for analyzing data and consent to users to examine data from a lot of unusual magnitude or angles, classification, and sum up the associations recognized.
BACKGROUND.
Data Mining and Knowledge Discovery in Databases are terms used interchangeably. Other terms often used are data or information harvesting, data archeology, functional dependency analysis, knowledge extraction and data pattern analysis. A high level definition of Data Mining is: the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data. Data mining is not a simple process and there is no tool that can do the job automatically. Data mining can be aided by tools, but it requires both human data mining expertise and human domain expertise. Data mining consists of a number of operations, each of which are supported by a variety of technologies, such as rule induction, neural networks, conceptual clustering. In real world applications information extraction requires the cooperative use of several data mining operations and techniques
STAGES OF DATA MINING
There are three stages of data mining, which are;
(i)Data Exploration
-This stage usually initiates with data arrangements which involve cleaning of data and data transformation into useful ways. In this stage a methodology way in which techniques are manual consumed to find one's way through a data set and bring about imperative aspects of that data into focal point for further investigation.
Though such line of attack can be applied to data sets of any size or types when its manual nature makes it more reasonable for less important data set with especially those in which the data has been carefully gathered and constructed as well.
(ii)Model building and validation: This stage absorb in considering various models and prefer the best one based on their predictive performance can be high-quality for use.
(iii)Deployment:
This is the final stage which involves in using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcomes
ADVANTAGES OF DATA MINING
The key reason why Data Mining is such a slogan at the moment is that because many organizations recognized the need to better understand their customers. Data mining can deliver real world results. Data mining has been used for the following types of applications:
(i)Understanding purchasing behaviors of customers
(ii)Detecting credit card or insurance fraud
(iii)Predicting probable changes in financial markets
PROBLEMS OF DATA MINING
One of the problems with data mining software has been the rush of companies to jump on the band wagon as these companies have slapped `data warehouse' labels on traditional transaction-processing products, and co-opted the dictionary of the industry in order to be considered players in this fast-growing category.
Many company Systems have established a criterion for a relational database management system (RDBMS) suitable for data warehousing, and documented specialized requirements for an RDBMS to qualify as a relational data warehouse server. According to most of company system, the requirements for data warehouse RDBMSs begin with the loading and preparation of data for query and analysis. If a product fails to meet the criteria at this stage, the rest of the system will be inaccurate, unreliable and unavailable.
Also Data mining systems rely on databases to supply the raw data for input and this raises problems in that databases tend be dynamic, incomplete, noisy, and large. Other problems arise as a result of the adequacy and relevance of the information stored.
Conclusion
Data mining is typically not used as a business system delivery technology.
Rather it is an extremely powerful and effective set of technologies for analyzing and clustering data which can be used to form the basis of a system
By Venance.
Am very interested with the presentation topic of Data Mining which was accessible by
Mr. Joshua Shendu &
Remy Kaaro
On 17/04/2009 there was the occurrence of the presented topic which was the best and well comprehended. This topic is less smooth but I came to appreciate that is excellent and well due to the fact that the presenters were prepared to do their task!
THE MEARNING OF DATA MINING. Data mining.
- Data Mining (also known as Knowledge Discovery) technology helps businesses discover hidden data patterns and provides predictive information which can be applied to benefit the business
Or
-As the process of investigate data from different point of view and shortening it into useful information that can be used to increase income, cuts expenses, or both. Or is the process of finding correlations or patterns among dozens of fields in large relational databases.
Data mining software is analytical tool used for analyzing data and consent to users to examine data from a lot of unusual magnitude or angles, classification, and sum up the associations recognized.
BACKGROUND.
Data Mining and Knowledge Discovery in Databases are terms used interchangeably. Other terms often used are data or information harvesting, data archeology, functional dependency analysis, knowledge extraction and data pattern analysis. A high level definition of Data Mining is: the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data. Data mining is not a simple process and there is no tool that can do the job automatically. Data mining can be aided by tools, but it requires both human data mining expertise and human domain expertise. Data mining consists of a number of operations, each of which are supported by a variety of technologies, such as rule induction, neural networks, conceptual clustering. In real world applications information extraction requires the cooperative use of several data mining operations and techniques
STAGES OF DATA MINING
There are three stages of data mining, which are;
(i)Data Exploration
-This stage usually initiates with data arrangements which involve cleaning of data and data transformation into useful ways. In this stage a methodology way in which techniques are manual consumed to find one's way through a data set and bring about imperative aspects of that data into focal point for further investigation.
Though such line of attack can be applied to data sets of any size or types when its manual nature makes it more reasonable for less important data set with especially those in which the data has been carefully gathered and constructed as well.
(ii)Model building and validation: This stage absorb in considering various models and prefer the best one based on their predictive performance can be high-quality for use.
(iii)Deployment:
This is the final stage which involves in using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcomes
ADVANTAGES OF DATA MINING
The key reason why Data Mining is such a slogan at the moment is that because many organizations recognized the need to better understand their customers. Data mining can deliver real world results. Data mining has been used for the following types of applications:
(i)Understanding purchasing behaviors of customers
(ii)Detecting credit card or insurance fraud
(iii)Predicting probable changes in financial markets
PROBLEMS OF DATA MINING
One of the problems with data mining software has been the rush of companies to jump on the band wagon as these companies have slapped `data warehouse' labels on traditional transaction-processing products, and co-opted the dictionary of the industry in order to be considered players in this fast-growing category.
Many company Systems have established a criterion for a relational database management system (RDBMS) suitable for data warehousing, and documented specialized requirements for an RDBMS to qualify as a relational data warehouse server. According to most of company system, the requirements for data warehouse RDBMSs begin with the loading and preparation of data for query and analysis. If a product fails to meet the criteria at this stage, the rest of the system will be inaccurate, unreliable and unavailable.
Also Data mining systems rely on databases to supply the raw data for input and this raises problems in that databases tend be dynamic, incomplete, noisy, and large. Other problems arise as a result of the adequacy and relevance of the information stored.
Conclusion
Data mining is typically not used as a business system delivery technology.
Rather it is an extremely powerful and effective set of technologies for analyzing and clustering data which can be used to form the basis of a system
By Venance.
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