Television – Monitoring – testing – or measuring – Transmission path testing
Reexamination Certificate
1998-11-05
2003-01-28
Tran, Phuoc (Department: 2621)
Television
Monitoring, testing, or measuring
Transmission path testing
C382S141000, C382S155000, C704S228000, C704S229000
Reexamination Certificate
active
06512538
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to signal processing. It is of application to the testing of communications systems and installations, and to other uses as will be described. The term “communications system” covers telephone or television networks and equipment, public address systems, computer interfaces, and the
2. Description of Related Art
It is desirable to use objective, repeatable, performance metrics to assess the acceptability of performance at the design, commissioning, and monitoring stages of communications services provision. However, subjective audio and video quality is central in determining customer satisfaction with products and services, so measurement of this aspect of the system's performance is important. The complexity of modern communications and broadcast systems, which may contain data reduction, renders conventional engineering metrics inadequate for the reliable prediction of perceived performance. Subjective testing can be used but is expensive, time consuming and often impractical particularly for field use. Objective assessment of the perceived (subjective) performance of complex systems has been enabled by the development of a new generation of measurement techniques, which take account of the properties of the human senses. For example, a poor signal-to-noise performance may result from an audible distortion, or from an inaudible distortion. A model of the masking that occurs in hearing is capable of distinguishing between these two cases.
Using models of the human senses to provide improved understanding of subjective performance is known as perceptual modelling.
The present applicant has a series of previous applications referring to perceptual models, and test signals suitable for non-linear speech systems:
WO 94/00922 Speech-like test-stimulus and perception based analysis to predict subjective performance.
WO 95/01011 Improved artificial-speech test-stimulus.
WO95/15035 Improved perception-based analysis with algorithmic interpretation of audible error subjectivity
To determine the subjective relevance of errors in audio systems, and particularly speech systems, assessment algorithms have been developed based on models of human hearing. The prediction of audible differences between a degraded signal and a reference signal, can be thought of as the sensory layer of a perceptual analysis, while the subsequent categorisation of audible errors can be thought of as the perceptual layer. Models for assessing high quality audio, such as described by Paillard B, Mabilleau P, Morissette S, and Soumagne J, in “PERCEVAL: Perceptual Evaluation of the Quality of Audio Systems.”,
J. Audio Eng. Soc
., Vol. 40, No. ½, January/Febuary 1992, have tended only to predict the probability of detection of audible errors since any audible error is deemed to be unacceptable, while early speech models have tended to predict the presence of audible errors and then employ simple distance measures to categorise their subjective importance, e.g.
Hollier M P, Hawksford M O, Guard D R, “Characterisation of Communications Systems Using a Speech-Like Test Stimulus”,
J. Audio Eng. Soc
., Vol. 41, No. 12, December 1993.
Beerends J, Stemerdink J, “A Perceptual Audio Quality Measure Based on a Psychoacoustic Sound Representation”,
J. Audio Eng. Soc
., Vol. 40, No. 12, December 1992.
Wang S, Sekey A, Gersho A, “An Objective Measure for Predicting Subjective Quality of Speech Coders”,
IEEE J. on Selected areas in Communications
, Vol. 10, No. 5, June 1992
It has been previously shown by Hollier M P, Hawksford M O, Guard D R, in “Error-activity and error entropy as a measure of psychoacoustic significance in the perceptual domain”,
IEE Proc
.-
Vis. Image Signal Process
., Vol. 141, No. 3, June 1994 that a more sophisticated description of the audible error provides an improved correlation with. subjective performance. In particular, the amount of error, distribution of error, and correlation of error with original signal have beer-shown to provide an improved prediction of error subjectivity.
FIG. 1
shows a hypothetical fragment of an error surface. The error descriptors used to predict the subjectivity of this error are necessarily multi-dimensional: no simple single dimensional metric can map between the error surface and the corresponding subjective opinion. The error descriptors, E
d
, are in the form:
E
d1
=fn
1
{e
(
i,j
)},
where fn
1
is a function of the error surface element values for descriptor
1
. For example the error descriptor for the distribution of the error, Error-entropy (E
a
), proposed by Hollier et al in the 1994 article cited above, was given by:
E
e
=
∑
i
=
1
n
∑
j
=
1
m
a
(
i
,
j
)
ln
a
(
i
,
j
)
where: a(i,j)=|e(i,j)|/E
a
and: E
a
is the sum of |e(i,j)| with respect to time and pitch.
Opinion prediction=fn
2
{E
d1
, E
d2
, . . . , E
dn
}
where fn
2
is the mapping function between the n error descriptors and the opinion scale of interest.
It has been shown that a judicious choice of error descriptors can be mapped to a number of different subjective opinion scales [Hollier M P, Sheppeard P J, “Objective speech quality assessment: towards an engineering metric”, Presented at the 100th AES Convention in Copenhagen, Preprint No. 4242, May 1996]. This is an important result since the error descriptors can be mapped to different opinion scales that are dominated by different aspects of error subjectivity. This result, together with laboratory experience, is taken to indicate that it is possible to weight a set of error descriptors to describe a range of error subjectivity since different features of the error are dominant for quality and effort opinion scales. The general approach of dividing the model architecture into sensory and perceptual layers and generating error descriptors that are sensitive to different aspects of error subjectivity is validated by these results.
A number of visual perceptual models are also under development and several have been proposed in the literature. For example, Watson A B, and Solomon J A, “Contrast gain control model fits masking data”.
ARVO
,. 1995 propose the use of Gabor functions to account for the inhibitory and excitatory influences of orientation between masker and maskee. Ran X, and Farvadin N, “A perceptually motivated three-component image model-Part I: Description of the model”,
IEEE transactions on image processing
, Vol. 4, No. 4 April 1995 use a simple image decomposition into edges, textures and backgrounds. However, most of the published algorithms only succeed in optimising individual aspects of model behaviour; Watson & Solomon provide a good model of masking, and Ran & Farvadin a first approximation to describing the subjective importance of errors.
An approach similar to that of the auditory perceptual model described above has been adopted by the present applicant for a visual perceptual model. A sensory layer reproduces the gross psychophysics of the sensory mechanisms:
(i) spatio-temporal sensitivity known as the “human visual filter”, and
(ii) masking due to spatial frequency, orientation and temporal frequency. Following the sensory layer the image is decomposed to allow calculation of error subjectivity, by the perceptual layer, according to the importance of errors in relation to structures within the image, as will now be described with reference to FIG.
2
. The upper part of
FIG. 2
illustrates an image to be decomposed, whilst lower part shows the decomposed image for error subjectivity prediction. If the visible error coincides with a critical feature of the image, such as an edge, then it is more subjectively disturbing. The basic image elements, which allow a human observer to perceive the image content, can be thought of as a set of abstracted boundaries. These boundaries can be formed by colour differences, texture changes and movement as well as edges,
Bayat Ali
British Telecommunications public limited company
Nixon & Vanderhye P.C.
Tran Phuoc
LandOfFree
Signal processing does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Signal processing, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Signal processing will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3048260