JPEG-based Image Compression Technology
JPEG is a standardized image compression mechanism. It stands for Joint Photographic Experts Group, the original name of the committee that wrote the standard. JPEG is designed for compressing either full-color or gray-scale images of natural, real-world scenes. It works well on photographs, naturalistic artwork, and similar material; not so well on lettering, simple cartoons, or line drawings. JPEG is a lossy compression algorithm, meaning that the decompressed image isn’t quite the same as the one you started with. JPEG is designed to exploit known limitations of the human eye (more about this later), notably the fact that small color changes are perceived less accurately than small changes in brightness. A useful property of JPEG is that the degree of lossiness can be varied by adjusting compression parameters. This means that the image maker can trade off file size against output image quality. The code we have developed includes:
- Color space transformation between RGB and YCbCr
- Quantization
- Optimized encoding
The JPEG compression algorithm is at its best on photographs and paintings of realistic scenes with smooth variations of tone and color. For web usage, where the bandwidth used by an image is important, JPEG is very popular. JPEG is the most common format saved by digital cameras. On the other hand, JPEG is not as well suited for line drawings and other textual or iconic graphics, where the sharp contrasts between adjacent pixels cause noticeable artifacts. Such images are better saved in a lossless graphics format such as TIFF, GIF, PNG, or a raw image format. JPEG is also not well suited to files that will undergo multiple edits, as some image quality will usually be lost each time the image is decompressed and recompressed (generation loss). To avoid this, an image that is being modified or may be modified in the future can be saved in a lossless format, and a copy exported as JPEG for distribution.
Index Terms: Matlab, source, code, JPEG, image, compression, DCT, quantization, coding, encoding, decoding, color, conversion.
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Figure 1. JPEG image |
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A simple and effective source code for JPEG Image Compression. |
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Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.
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1.0
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2008.12.24 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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JPEG Image Compression – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 35 EUROS (less than 49 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of JPEG Image Compression. Alternatively, you can bestow using our banking coordinates:
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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 14 SP1. Matlab Image Processing Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 1% [?]
Linear Predictive Coding for Stock Market Forecasting
Linear predictive coding (LPC) is a tool used mostly in audio signal processing and speech processing for representing the spectral envelope of a digital signal of speech in compressed form, using the information of a linear predictive model. It is one of the most powerful speech analysis techniques, and one of the most useful methods for encoding good quality speech at a low bit rate and provides extremely accurate estimates of speech parameters. LPC is generally used for speech analysis and resynthesis. It is used as a form of voice compression by phone companies, for example in the GSM standard. It is also used for secure wireless, where voice must be digitized, encrypted and sent over a narrow voice channel, an early example of this is the US government’s Navajo I.
We have developed an advanced approach based on LPC for stock market forecasting. Code, given input data, is able to say if next closing price will be higher or lower than last closing price. A buy signal is given if next closing price will be higher than current price. A sell signal is given if next closing price will be equal or lower than current price. This algorithm has been tested on Italian securities (more than 320 securities). Data have been imported from Yahoo. Half of data have been used for training and the remaining ones for testing, with no overlapping between training and testing data. We have obtained an excellent recognition rate of 56.02%.
Index Terms: Matlab, source, code, lpc, linear, predictive, coding, stock, market, forecasting, forecast, security.
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Figure 1. Forecasting financial data |
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A simple and effective source code for Advanced LPC Trading System. |
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Demo code (protected P-files) available for performance evaluation. Matlab Signal Processing Toolbox is required.
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1.0
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2009.07.08 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Advanced LPC Trading System. Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 1700 EUROS (less than 2380 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of Advanced LPC Trading System. Alternatively, you can bestow using our banking coordinates:
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The authors are not commodity trading advisors. The information on this site is for trading education only. There are no trading recommendations for any one individual made on this site and this information is paper trades for trading education. All trades are extremely risky and only risk capital should be used when trading. The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Signal Processing Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 1% [?]
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