639 research outputs found
Image data compression application to imaging spectrometers
The potential of image data compression techniques to satisfy the anticipated requirements of imaging spectrometer missions is discussed. Noiseless coding, rate controlled compression, cluster compression, and error protection are addressed
Time and frequency domain algorithms for speech coding
The promise of digital hardware economies (due to recent advances in
VLSI technology), has focussed much attention on more complex and sophisticated
speech coding algorithms which offer improved quality at relatively
low bit rates.
This thesis describes the results (obtained from computer simulations)
of research into various efficient (time and frequency domain) speech
encoders operating at a transmission bit rate of 16 Kbps.
In the time domain, Adaptive Differential Pulse Code Modulation (ADPCM)
systems employing both forward and backward adaptive prediction were
examined. A number of algorithms were proposed and evaluated, including
several variants of the Stochastic Approximation Predictor (SAP). A
Backward Block Adaptive (BBA) predictor was also developed and found to
outperform the conventional stochastic methods, even though its complexity
in terms of signal processing requirements is lower. A simplified
Adaptive Predictive Coder (APC) employing a single tap pitch predictor
considered next provided a slight improvement in performance over ADPCM,
but with rather greater complexity.
The ultimate test of any speech coding system is the perceptual performance
of the received speech. Recent research has indicated that this
may be enhanced by suitable control of the noise spectrum according to
the theory of auditory masking. Various noise shaping ADPCM
configurations were examined, and it was demonstrated that a proposed
pre-/post-filtering arrangement which exploits advantageously the
predictor-quantizer interaction, leads to the best subjective
performance in both forward and backward prediction systems.
Adaptive quantization is instrumental to the performance of ADPCM systems.
Both the forward adaptive quantizer (AQF) and the backward oneword
memory adaptation (AQJ) were examined. In addition, a novel method
of decreasing quantization noise in ADPCM-AQJ coders, which involves the
application of correction to the decoded speech samples, provided
reduced output noise across the spectrum, with considerable high frequency
noise suppression.
More powerful (and inevitably more complex) frequency domain speech
coders such as the Adaptive Transform Coder (ATC) and the Sub-band Coder
(SBC) offer good quality speech at 16 Kbps. To reduce complexity and
coding delay, whilst retaining the advantage of sub-band coding, a novel
transform based split-band coder (TSBC) was developed and found to compare
closely in performance with the SBC.
To prevent the heavy side information requirement associated with a
large number of bands in split-band coding schemes from impairing coding
accuracy, without forgoing the efficiency provided by adaptive bit
allocation, a method employing AQJs to code the sub-band signals together
with vector quantization of the bit allocation patterns was also
proposed.
Finally, 'pipeline' methods of bit allocation and step size estimation
(using the Fast Fourier Transform (FFT) on the input signal) were examined.
Such methods, although less accurate, are nevertheless useful in
limiting coding delay associated with SRC schemes employing Quadrature
Mirror Filters (QMF)
Speech coding at medium bit rates using analysis by synthesis techniques
Speech coding at medium bit rates using analysis by synthesis technique
Joint source channel coding for progressive image transmission
Recent wavelet-based image compression algorithms achieve best ever performances with fully embedded bit streams. However, those embedded bit streams are very sensitive to channel noise and protections from channel coding are necessary. Typical error correcting capability of channel codes varies according to different channel conditions. Thus, separate design leads to performance degradation relative to what could be achieved through joint design. In joint source-channel coding schemes, the choice of source coding parameters may vary over time and channel conditions. In this research, we proposed a general approach for the evaluation of such joint source-channel coding scheme. Instead of using the average peak signal to noise ratio (PSNR) or distortion as the performance metric, we represent the system performance by its average error-free source coding rate, which is further shown to be an equivalent metric in the optimization problems.
The transmissions of embedded image bit streams over memory channels and binary symmetric channels (BSCs) are investigated in this dissertation. Mathematical models were obtained in closed-form by error sequence analysis (ESA). Not surprisingly, models for BSCs are just special cases for those of memory channels. It is also discovered that existing techniques for performance evaluation on memory channels are special cases of this new approach. We further extend the idea to the unequal error protection (UEP) of embedded images sources in BSCs. The optimization problems are completely defined and solved. Compared to the equal error protection (EEP) schemes, about 0.3 dB performance gain is achieved by UEP for typical BSCs. For some memory channel conditions, the performance improvements can be up to 3 dB. Transmission of embedded image bit streams in channels with feedback are also investigated based on the model for memory channels. Compared to the best possible performance achieved on feed forward transmission, feedback leads to about 1.7 dB performance improvement
A comparison of adaptive predictors in sub-band coding
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1987.Bibliography: leaves 82-85.by Paul Ning.M.S
Scalable video compression
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1992.Includes bibliographical references (leaves 85-88).by Joseph Bruce Stampleman.M.S
Real-time filtering and detection of dynamics for compression of HDTV
The preprocessing of video sequences for data compressing is discussed. The end goal associated with this is a compression system for HDTV capable of transmitting perceptually lossless sequences at under one bit per pixel. Two subtopics were emphasized to prepare the video signal for more efficient coding: (1) nonlinear filtering to remove noise and shape the signal spectrum to take advantage of insensitivities of human viewers; and (2) segmentation of each frame into temporally dynamic/static regions for conditional frame replenishment. The latter technique operates best under the assumption that the sequence can be modelled as a superposition of active foreground and static background. The considerations were restricted to monochrome data, since it was expected to use the standard luminance/chrominance decomposition, which concentrates most of the bandwidth requirements in the luminance. Similar methods may be applied to the two chrominance signals
Statistical framework for video decoding complexity modeling and prediction
Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time - feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding
Study and simulation of low rate video coding schemes
The semiannual report is included. Topics covered include communication, information science, data compression, remote sensing, color mapped images, robust coding scheme for packet video, recursively indexed differential pulse code modulation, image compression technique for use on token ring networks, and joint source/channel coder design
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