The intensity JND comes from Poisson neural noise: Implications for image coding

Allen, Jont (2000) The intensity JND comes from Poisson neural noise: Implications for image coding. [Conference Paper]

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While the problems of image coding and audio coding have frequently been assumed to have similarities, specific sets of relationships have remained vague. One area where there should be a meaningful comparison is with central masking noise estimates, which define the codec's quantizer step size. In the past few years, progress has been made on this problem in the auditory domain (Allen and Neely, J. Acoust. Soc. Am., {\bf 102}, 1997, 3628-46; Allen, 1999, Wiley Encyclopedia of Electrical and Electronics Engineering, Vol. 17, p. 422-437, Ed. Webster, J.G., John Wiley \& Sons, Inc, NY). It is possible that some useful insights might now be obtained by comparing the auditory and visual cases. In the auditory case it has been shown, directly from psychophysical data, that below about 5 sones (a measure of loudness, a unit of psychological intensity), the loudness JND is proportional to the square root of the loudness $\DL(\L) \propto \sqrt{\L(I)}$. This is true for both wideband noise and tones, having a frequency of 250 Hz or greater. Allen and Neely interpret this to mean that the internal noise is Poisson, as would be expected from neural point process noise. It follows directly that the Ekman fraction (the relative loudness JND), decreases as one over the square root of the loudness, namely $\DL/\L \propto 1/\sqrt{\L}$. Above ${\L} = 5$ sones, the relative loudness JND $\DL/\L \approx 0.03$ (i.e., Ekman law). It would be very interesting to know if this same relationship holds for the visual case between brightness $\B(I)$ and the brightness JND $\DB(I)$. This might be tested by measuring both the brightness JND and the brightness as a function of intensity, and transforming the intensity JND into a brightness JND, namely \[ \DB(I) = \B(I+ \DI) - \B(I) \approx \DI \frac{d\B}{dI}. \] If the Poisson nature of the loudness relation (below 5 sones) is a general result of central neural noise, as is anticipated, then one would expect that it would also hold in vision, namely that $\DB(\B) \propto \sqrt{\B(I)}$. %The history of this problem is fascinating, starting with Weber and Fechner. It is well documented that the exponent in the S.S. Stevens' power law is the same for loudness and brightness (Stevens, 1961) \nocite{Stevens61a} (i.e., both brightness $\B(I)$ and loudness $\L(I)$ are proportional to $I^{0.3}$). Furthermore, the brightness JND data are more like Riesz's near miss data than recent 2AFC studies of JND measures \cite{Hecht34,Gescheider97}.

Item Type:Conference Paper
Keywords:Neural noise, Intensity JND, Poisson neural noise, image compression
Subjects:Psychology > Psychophysics
ID Code:1513
Deposited By: Allen, Jont
Deposited On:26 May 2001
Last Modified:11 Mar 2011 08:54


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