Abstract: Image compression can improve the performance of
the digital systems by reducing time and cost in image storage
and transmission without significant reduction of the image quality.
Furthermore, the discrete cosine transform has emerged as the new
state-of-the art standard for image compression. In this paper, a
hybrid image compression technique based on reversible blockade
transform coding is proposed. The technique, implemented over
regions of interest (ROIs), is based on selection of the coefficients
that belong to different transforms, depending on the coefficients is
proposed. This method allows: (1) codification of multiple kernals
at various degrees of interest, (2) arbitrary shaped spectrum,and (3)
flexible adjustment of the compression quality of the image and the
background. No standard modification for JPEG2000 decoder was
required. The method was applied over different types of images.
Results show a better performance for the selected regions, when
image coding methods were employed for the whole set of images.
We believe that this method is an excellent tool for future image
compression research, mainly on images where image coding can
be of interest, such as the medical imaging modalities and several
multimedia applications. Finally VLSI implementation of proposed
method is shown. It is also shown that the kernal of Hartley and
Cosine transform gives the better performance than any other model.
Abstract: Vector quantization is a powerful tool for speech
coding applications. This paper deals with LPC Coding of speech
signals which uses a new technique called Multi Switched Split
Vector Quantization, This is a hybrid of two product code vector
quantization techniques namely the Multi stage vector quantization
technique, and Switched split vector quantization technique,. Multi
Switched Split Vector Quantization technique quantizes the linear
predictive coefficients in terms of line spectral frequencies. From
results it is proved that Multi Switched Split Vector Quantization
provides better trade off between bitrate and spectral distortion
performance, computational complexity and memory requirements
when compared to Switched Split Vector Quantization, Multi stage
vector quantization, and Split Vector Quantization techniques. By
employing the switching technique at each stage of the vector
quantizer the spectral distortion, computational complexity and
memory requirements were greatly reduced. Spectral distortion was
measured in dB, Computational complexity was measured in
floating point operations (flops), and memory requirements was
measured in (floats).
Abstract: Vector quantization is a powerful tool for speech
coding applications. This paper deals with LPC Coding of speech
signals which uses a new technique called Multi Switched Split
Vector Quantization (MSSVQ), which is a hybrid of Multi, switched,
split vector quantization techniques. The spectral distortion
performance, computational complexity, and memory requirements
of MSSVQ are compared to split vector quantization (SVQ), multi
stage vector quantization(MSVQ) and switched split vector
quantization (SSVQ) techniques. It has been proved from results that
MSSVQ has better spectral distortion performance, lower
computational complexity and lower memory requirements when
compared to all the above mentioned product code vector
quantization techniques. Computational complexity is measured in
floating point operations (flops), and memory requirements is
measured in (floats).
Abstract: This paper investigates the performance of a speech
recognizer in an interactive voice response system for various coded
speech signals, coded by using a vector quantization technique namely
Multi Switched Split Vector Quantization Technique. The process of
recognizing the coded output can be used in Voice banking application.
The recognition technique used for the recognition of the coded speech
signals is the Hidden Markov Model technique. The spectral distortion
performance, computational complexity, and memory requirements of
Multi Switched Split Vector Quantization Technique and the
performance of the speech recognizer at various bit rates have been
computed. From results it is found that the speech recognizer is
showing better performance at 24 bits/frame and it is found that the
percentage of recognition is being varied from 100% to 93.33% for
various bit rates.