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07-06-2009, 01:20 AM
ABSTRACT This paper aims at providing an efficient solution for image compression technique using Wavelet-based coding technique. The quality of compression lies only in the selection of wavelet filter and the decomposition level. This technique comprises of both the quantization and encoding. As both the processes are carried out in short time the reliability in compressing an image will be high by Fast Image Compression Technique. Since the truncation of the redundant data there is no any notable difference between the original image and synthesized image. This algorithm is not much complex and the memory required to implement the algorithm is not very high. With a strong mathematical background and quantitative analysis, it enables efficient compression in the growing trends. The compression using this algorithm can give compression ratios of approximately 25:1 without any noticeable image distortion or even 100:1, but with some image distortion. The above said algorithm has been simulated and implemented for the verification in the platforms of MATLAB.
Use Search at http://topicideas.net/search.php wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion
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12-04-2011, 11:36 AM
IMAGE COMPRESSION.ppt (Size: 590.5 KB / Downloads: 240)
Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages.
Methods of Image Compression:
Different Techniques of image compression:
Image compession tecnique for internet use:
For non internet use:
Why use image compression
Image compression is important for webmasters who want to create faster loading web pages which in turn will save a lot of bandwidth.
Image compression is important for people who attach photos to emails which will send the email more quickly, save on bandwidth costs and not make the recipient of the email angry.
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13-05-2011, 10:34 AM
DATA COMPRESSION.ppt (Size: 2.26 MB / Downloads: 148)
DATA COMPRESSION TECHNIQUES
Data compression is the process of encoding data so that it takes less storage space or less transmission time than it would if it were not compressed.
Compression is possible because most real-world data is very redundant
In lossless data compression, the integrity of the data is preserved, the original data and the data after compression and decompression are exactly the same.
The compression and decompression algorithms are exact inverses of each other: no part of the data is lost in the process.
Redundant data is removed in compression and added during decompression.
Lossless compression methods are normally used when we cannot afford to lose any data.
It is probably the simplest method of compression. It can be used to compress data made of any combination of symbols.
The general idea behind this method is to replace consecutive repeating occurrences of a symbol by one occurrence of the symbol followed by the number of occurrences.
The method can be even more efficient if the data uses only two symbols (for example 0 and 1) in its bit pattern and one symbol is more frequent than the other.
Run-length encoding example
Run-length encoding for two symbols
It assigns shorter codes to symbols that occur more frequently and longer codes to those that occur less frequently.
For example, imagine we have a text file that uses only five characters (A, B, C, D, E).
Before we can assign bit patterns to each character, we assign each character a weight based on its frequency of use.
Lempel Ziv encoding
It is an example of a category of algorithms called dictionary-based encoding.
The idea is to create a dictionary (a table) of strings used during the communication session
If both the sender and the receiver have a copy of the dictionary, then previously-encountered strings can be substituted by their index in the dictionary to reduce the amount of information transmitted.
LOSSY COMPRESSION METHODS
Our eyes and ears cannot distinguish subtle changes. In such cases, we can use a lossy data compression method.
These methods are cheaper—they take less time and space when it comes to sending millions of bits per second for images and video.
Several methods have been developed using lossy compression techniques.
JPEG (Joint Photographic Experts Group) encoding is used to compress pictures and graphics
MPEG (Moving Picture Experts Group) encoding is used to compress video
MP3 (MPEG audio layer 3) for audio compression.
An image can be represented by a two-dimensional array (table) of picture elements (pixels).
A grayscale picture of 307,200 pixels is represented by 2,457,600 bits, and a color picture is represented by 7,372,800 bits.
In JPEG, a grayscale picture is divided into blocks of 8 × 8 pixel blocks to decrease the number of calculations.
The whole idea of JPEG is to change the picture into a linear (vector) set of numbers that reveals the redundancies. The redundancies (lack of changes) can then be removed using one of the lossless compression methods we studied previously. A simplified version of the process is shown in Figure 15.11.
Discrete cosine transform (DCT)
In this step, each block of 64 pixels goes through a transformation called the discrete cosine transform (DCT).
The transformation changes the 64 values so that the relative relationships between pixels are kept but the redundancies are revealed.
The formula is given in Appendix G. P(x, y) defines one value in the block, while T(m, n) defines the value in the transformed block.
After the T table is created, the values are quantized to reduce the number of bits needed for encoding.
Quantization divides the number of bits by a constant and then drops the fraction. This reduces the required number of bits even more.
In most implementations, a quantizing table (8 by 8) defines how to quantize each value.
This is done to optimize the number of bits and the number of 0s for each particular application.
After quantization the values are read from the table, and redundant 0s are removed.
To cluster the 0s together, the process reads the table diagonally in a zigzag fashion rather than row by row or column by column.
JPEG usually uses run-length encoding at the compression phase to compress the bit pattern resulting from the zigzag linearization.
The Moving Picture Experts Group (MPEG) method is used to compress video.
A motion picture is a rapid sequence of a set of frames in which each frame is a picture.
In other words, a frame is a spatial combination of pixels, and a video is a temporal combination of frames that are sent one after another.
Audio compression can be used for speech or music. For speech we need to compress a 64 kHz digitized signal, while for music we need to compress a 1.411 MHz signal.
Two categories of techniques are used for audio compression:
1: predictive encoding
2: perceptual encoding.
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02-06-2011, 01:49 PM
hey...may i know your matlab codes which you have used in this project and implimentation for image compression
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16-02-2012, 12:23 PM
to get information about the topic image compression full report ppt and related topic refer the link bellow
http://topicideas.org/how-to-fractal-ima...on-seminar and presentation-report
http://topicideas.org/how-to-image-proce...ll-seminar and presentation-report?page=2
http://seminar and presentationproject and implimentations.com/attachment.php?aid=476
Joined: Apr 2012
21-04-2012, 10:44 AM
1.INT TO IMAGE.docx (Size: 24.43 KB / Downloads: 20)
Image compression means minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in the file size allows more image to be stored in a given amount of disk more memory space. It also reduces the time required for image to be sent over the internet or downloaded from web pages. Uncompressed multimedia (graphics, audio and video) data requires considerable storage capacity and bandwidth. Despite rapid progress in mass storage density, processor speeds, and digital communication system performance, demand for data storage capacity and data transmission bandwidth continues to outstrip the capabilities of available technology. The recent growth of data intensive multimedia based web application have not only sustained the need for more efficient ways to encode signals and images but have made compression of such signal central to storage and communication technology.
NEED FOR COMPRESSION
An image 1024 pixel *1024 pixel *1024 pixel *24 bit ,without compression would require 3MB of storage and 7 minute of transmission, utilizing a high speed ,64 kbs, ISDN line .If the image is compressed at a 10:1 compression ratio, the storage requirement is reduced to 300 KB and the transmission time is reduced to less than 7 second. Images file in an uncompressed Form are very large, and the internet especially for people using a 56 kbps dialup modem, can be pretty slow. This combination could seriously limit one of the webs most appreciated aspects –its ability to present images easily.
The examples above clearly illustrate the need for sufficient storage space, large transmission bandwidth, and long transmission time for image . Audio and video data at present, the only solution is to compress multimedia data before its storage and transmission and decompress it at the receiver.
TYPES OF COMPRESSION
In the case of video, compression ratio causes some of the information to be lost some information at a detailed level is considered not essential for a reasonable reproduction of scene.This type of compression is called lossy compression, audio compression on the other hand is not lossy, it is called loss less compression.
1.3.1 Lossless Compression
In lossless compression scheme, the reconstructed image after the compression is numerically identical to original image. However lossless compression can only achieve a modest amount of compression. Lossless coding guaranties that the decompressed image is absolutely identical to the image before compression. This is an important requirement for some application domain, e.g. medical imaging, where not only high quality is in demand but unaltered archiving is a legal requirement. Lossless technique can also be used for the compression of other data types where loss of information is not acceptable, e.g. text document and program executables.
Lossy is a term applied to data compression technique in which some amount of the Original data is lost during the compression process. Lossy image compression applications attempt to eliminate redundant or un necessary information in terms of what the human eye can perceive. An image reconstructed following lossy compression contains degradation relative to the original image. Often this is because the compression scheme completely discards redundant information. However lossy scheme are capable of achieving much higher compression. Under normal viewing condition, no visible loss is perceived (visually lossless).
Lossy image data compression is useful for application to the world wide images for Quicker transfer across the internet. An image reconstructed following lossy compression contains degradation relative to the original. Often this is because the compression scheme completely discard the redundant information.
APPLICATIONS OF COMPRESSION
Applications of data compression are primarily in the transmission and in storage of information. Image transmission application are in broadcast television remote sensing via satellite, military application via aircraft, radar and sonar, teleconferencing, computer communication, facsimile transmission, etc. Image storage is required for educational business documents, medical images, that rises in computer tomography, magnetic resonance imaging and digital radiology, motion picture, satellite image weather maps, etc. Application of data compression is also possible in the development of fast algorithm, where the number of operations required to be implement an algorithm is reduced by working with compressed data. Over the year, the need for image compression has grown steadily. Currently it is recognized as an enabling technology. It plays a crucial role in many important and diverse applications such as:
1. Business document, where lossy compression is prohibited for legal reasons.
2. Satellite image where the data loss is undesirable because of image collect cost.
3. Medical image where difference in original image and uncompressed one can compress
4. Tele -video conferencing.
5. Remote sensing.
6. Space and hazardous waste water application .
7. Control of remotely piloted vehicle in military.
8. Facsimile transmission (fax)
CHARACTERISTIC OF COMPRESSION:
Image quality describes the fidelity with which an image compression scheme recreates the Source image data. There are four main characteristics to judge image compression algorithms
1. Compression Ratio
2. Compression Speed
3. Mean Square Error
4. Peak Signal to Noise Ratio
These characteristics are used to determine the suitability of a given compression algorithm for any application.
The compression ratio is equal to the size of the original image divided by the size of the compressed image. This ratio gives how much compression is achieved for a particular image. The compression ratio achieved usually indicates the picture quality. Generally, the higher the compression ratio, the poorer the quality of the resulting image. The trade off between compression ratio and picture quality is an important one to consider when compressing images.
Joined: Apr 2012
07-05-2012, 01:42 PM
image compression.pptx (Size: 486.54 KB / Downloads: 29)
Image is an artifact which means an object produced by human .they are captured by digital camera,scanner ,human eye etc .
this is an image where the pixels can have one of two values,referred as black and white.each pixel is represented by 1 bit.
a pixel in such image can have n values through 0-(n-1),indicating 2n shades of gray.value of n is compatible with a byte size i.e 4,8,12 so on.
- this type of image can have many similar colors. When adjectent pixels differ by one unit,it is hard to distinguish their colors.it is normaly a natural image obtained by taking a photograph with digital camera.
- it is an artifical image.it have few or many colors,but it does not have the noise and blurring of a image.
A consecutive repeated string of character is replaced by two bytes.
First byte contain number representing number of times the character is repeated.
Second byte contain the character itself.
Mainly used for TIFF, BMP files.
Joined: Apr 2012
26-05-2012, 03:10 PM
Image Compression.ppt (Size: 935 KB / Downloads: 54)
Why Do We Need Compression
Requirements may outstrip the anticipated increase of storage space and bandwidth
For data storage and data transmission
The bit rate of uncompressed digital cinema data exceeds 1 Gbps
Why Can We Compress?
Neighboring pixels are not independent but correlated
Reduction of the number of bits needed to
represent a given image or it’s information
exploits the fact that all images are not
Exploits energy gaps in signal
Selected Methods for compression
Bit plane encoding
Why Do We Need International Standards?
International standardization is conducted to achieve inter-operability .
Only syntax and decoder are specified.
Encoder is not standardized and its optimization is left to the manufacturer.
Standards provide state-of-the-art technology that is developed by a group of experts in the field.
Not only solve current problems, but also anticipate the future application requirements.
What Is JPEG?
"Joint Photographic Expert Group". Voted as international standard in 1992.
Works with color and grayscale images, e.g., satellite, medical, ...
Lossy and lossless
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