Discrete Cosine Transform: Algorithms, Advantages, Applications - Kindle edition by K. Ramamohan Rao, P. Yip. Download it once eBook features: Highlight. Discrete Cosine Transform: Algorithms, Advantages, Applications eBook: K. Ramamohan Rao, P. Yip: nanofusmortsubc.tk: Kindle Store. Discrete Cosine Transform - 1st Edition - ISBN: , Algorithms, Advantages, Applications eBook ISBN:
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Discrete Cosine Transform: Algorithms, Advantages, Applications K. Ramamohan Rao, P. Yip extensive bibliography covers both the theory and applications of the DCT. Applications by K. Ramamohan Rao, P. Yip for online ebook. This is the first comprehensive treatment of the theoretical aspects of the discrete cosine transform (DCT), which is being recommended by various standards. Advantages, Applications [Book] PDF Ó. Read Online. Fast Transforms Algorithms Analyses Applications,. Discrete Cosine Transform - Sciencedirect, Discrete.
Generalized kraft inequality and arithmetic coding. IBM J. Langdon, G. A simple general binary source code. Yaroslavsky, L.: SPIE, vol. Ponomarenko, N. Malvar, H. The LOT: Tran, T. The generalized lapped biorthogonal transform.
Personalised recommendations. Cite paper How to cite? ENW EndNote. His many articles have been published and available online at Scribed, Lulu publication, Slide share, site. His field of research and interest are Digital signal processing, Digital Image Processing, Audio signal processing, Wavelets and filter design. Saxena by whom hard working capacity and consistency has been transferred into me. My wife Pooja who always appreciate me to do good job and for ignoring all of my mistakes committed by me knowingly or unknowingly and My cute daughter Poorvi who developed into me a sense of responsibility and patience.
I am very thankful to my wife and co author of this book prof. I am very grateful for every person by whom I could learn. I am also grateful for all the situation and incidences by which I learn something Again I want to say thanks technology Google search engine and internet which help me a lot to understand this subject and help me to share my views to others. I am also thankful to all the authors and writers who share their research work with us.
This data should not be hearable to human ear and it should robust, so that it could be used for the purpose of Intellectual Property Rights. It contains useful certifiable information for the owner of the host media, such as producer's name, company logo, etc. Audio watermarking is a technique that hides copyright information into the digital audio signal.
Embedded data not only must be imperceptible but also should resist attacks and other types of distortions trying to remove or neutralize the watermark picture. Audio watermarking is used to hide information in audio signal.
In the process of watermarking there are two signals  Host signal: The original signal for example audio, video, image, text which is to be protected from unauthorized copying and distribution.
Fig 1. As a result, the music industry claims a multibillion dollar annual revenue loss due to piracy. Normally an application is developed by a person or a small group of people and used by many.
Hackers are the people who tend to change the original application by modifying it or use the same application to make profits without giving credit to the owner.
So we do require a technique which can protect our data from unauthorized copying and distribution and can provide copyright owner identification for our digital data over internet. Digital watermarking technology is now drawing attention as a new method of protecting unauthorized copying of digital content. A digital watermark is an imperceptible signal added to digital multimedia data namely, audio, video, or image , which should remain even after several signal processes or potential attacks.
According to the detection process 1 Blind watermarking: if original signal is not required for the extraction of watermark then it is known as blind watermarking.
Blind watermarking is also known as public watermarking. Non blind watermarking is also known as private watermarking 1. The embedded information is robust and secure against attacks and can be demonstrated in a case of dispute of ownership.
There can be the situations where some other person modifies the embedded watermark and claims that it is his own. In such cases the actual owner can use the watermark to show the actual proof of ownership. This situation is important because it is necessary to know about the tampering caused to the media signal.
The tampering is sometime a cause of forging of the watermark which has to be avoided. In the content authentication applications, a set of secondary data is embedded in the host multimedia signal and is later used to determine whether the host signal was tampered.
After a watermark has been detected and content decoded, the copy control or access control policy is enforced by directing particular hardware or software operations such as enabling or disabling the record module. These applications require watermarking algorithms resistant against intentional attacks and signal processing modifications, able to perform a blind watermark detection and capable of embedding a non-trivial number of bits in the host signal. While the robustness against intentional attack is not required, a certain degree of robustness against common processing like MPEG compression may be desired.
A public watermark embedded into the host multimedia might be used as the link to external databases that contain certain additional information about the multimedia file itself, such as copyright information and licensing conditions [V]Finger printing Additional data embedded by a watermark in the fingerprinting applications are used to trace the originator or recipients of a particular copy of a multimedia file.
The usage of an audio file can be recorded by a fingerprinting system. When a file is accessed by a user, a watermark, or called fingerprint in this case, is embedded into the file thus creating a mark on the audio.
The usage history can be traced by extracting all the watermarks that were embedded into the file [VI]Broadcast monitoring A variety of applications for audio watermarking are in the field of broadcasting. Watermarking is an obvious alternative method of coding identification information for an active broadcast monitoring. It has the advantage of being embedded within the multimedia host signal itself rather than exploiting a particular segment of the broadcast signal.
Thus, it is compatible with the already installed base of broadcast equipment, including digital and analogue communication channels. This application is important because it is highly advisable to have the patients name entered on reports, and reduces the misplacements of reports which are very important during treatment.
The pilot communicates with a ground monitoring system through voice at a particular frequency. However, it can be easily trapped and attacked, and is one of the causes of miss communication. To avoid such problems, the flight number is embedded into the voice communication between the ground operator and the flight pilot.
As the flight numbers are unique the tracking of flights will become more secure and easy. The most significant requirements are perceptibility, robustness, security, reliability, capacity, and speed performance.
The signal to noise ratio SNR of the watermarked signal to the original signal should be maintained greater than 20dB.
In addition, the technique should make the modified signal not perceivable by human ear. It is also important to know if the watermark is completely distributed over the host signal because, it is possible that near the extraction process a part of the signal is only available. Hence, capacity is also a primary concern in the real time situations. The speed of embedding of watermark is important in real time applications where the embedding is done on continuous signals such as speech of an official or conversation between airplane pilot and ground control staff.
Some of the possible applications where speed is a constraint are audio streaming and airline traffic monitoring. Both embedding and extraction process need to be made as fast as possible with greater efficiency. The security of a watermark refers to its ability to resist hostile attacks.
The types of attacks can fall in three categories: unauthorized removal, unauthorized embedding, and unauthorized detection. The Cost of watermarking system refers to the speed with which embedding and detection must be performed and the number of embedded and detectors that must be deployed.
Other issues include the whether the detector and embedded are to be implemented as hardware device or as software application or plug-ins. Algorithm complexity is important to know, for it may influence the choice of implementation structure or DSP architecture. In this study, actual CPU timings in seconds of algorithm implementations were collected.
Thus, asymmetry is also a noticeable concern. It is recommended to have unique watermarks to different files to help make the technique more useful.
Compression of audio signal is a very common signal processing tool. Audio files are compressed when these files are stored at disk at server or transmitted over a communication channel.
Watermark should be robust against compression. Audio generation is done at a particular sampling frequency and bit rate however the created audio track will undergo so many different types of compression and conversion techniques. In addition to that, it is common process that the original audio signal will change its sampling frequencies like from Kbps to 64Kpbs or 48 Kbps.
There are some programs that can achieve these conversions and perform compression operation. During analog to digital conversion of an audio signal an audio signal is first sampled at Nyquest rate than quantized during the process of quantization a quantization error is introduced quantization error is simply the difference between the sampled value of the signal and quantized value of the signal.
The Quantization error is inversely proportional to the no of quantization level if we increase the no of quantization level then quantization error will be decreased and if we decrease the no of quantization level than quantization error increase.
Again as we increase the no of quantization level we do require a more no of bits to represent the quantized sample and as we decrease the no of quantization level we do require a less no of bit to represent the quantized value of sample. Hence there is a tradeoff between quantization error and no of bits to encode the sample i. View at Google Scholar T. Huang and O. Habibi and P. View at Google Scholar W.
Pennebaker and J. Richardson, H. Wiegand, G. Sullivan, G. Schelkens, A. Skodras, and T. Vaisey and A. View at Google Scholar D. Donoho and J. View at Google Scholar M. Malvar and D. Yaroslavsky, K. Egiazarian, and J. Yaroslavsky, B.
Fishbain, A. Shteinman, and S.
View at Scopus A. Buades, B. Coll, and J. Morel and A. Hecht and J. Dabov, A. Foi, V. Katkovnik, and K. Yaroslavsky and M. Katkovnik, K. View at Google Scholar E. Potter, G. Koppand, and H. View at Google Scholar R. Coifman and D. Antoniadis, Ed.
View at Google Scholar ftp: Shaick, L. Ridel, and L. Gabbouj and P.