The discrete wavelet transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression, measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that we call DWT uniform quantization noise; it is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r2/sup -/spl lambda//, where r is the display visual resolution in pixels/degree, and /spl lambda/ is the wavelet level. Thresholds increase rapidly with wavelet spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from lowpass to horizontal/vertical to diagonal. We construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.

Visibility of wavelet quantization noise

Accurate control of contrast on microcomputer displays.

The functions of the primate color-opponent and broad-band channels were assessed by examining the visual capacities of rhesus monkeys following selective lesions of parvocellular and magnocellular lateral geniculate nucleus, which respectively relay these two channels to the cortex. Parvocellular lesions impaired color vision, high spatial-frequency form vision, and fine stereopsis. Magnocellular lesions impaired high temporal-frequency flicker and motion perception but produced no deficits in stereopsis. Low spatial-frequency form vision, stereopsis, and brightness perception were unaffected by either lesion. Much as the rods and cones of the retina can be thought of as extending the range of vision in the intensity domain, we propose that the color-opponent channel extends visual capacities in the wavelength and spatial-frequency domains whereas the broad-band channel extends them in the temporal domain.

Role of the color-opponent and broad-band channels in vision.

To the Editor: As an advocate for patient safety and for the introduction of appropriate monitoring technology into perioperative practice, I read the recent article by Taenzer et al. with anticipation. Unfortunately, the conclusions reached by the authors do not appear to be supported by the evidence provided in the article. Specifically, the authors conclude “our results demonstrate that continuous patient surveillance can improve outcomes in a postoperative orthopedic ward setting.” There was no meaningful difference in death, intensive care unit transfer, or hospital stay; the only reported difference was in the number of “rescue” events. The rescue events consisted of several levels of intervention ranging from conventional code blue teams to a bedside visit of an intensive care nurse and a respiratory care technician within 10 min of call. Surely, the “rescues” at the latter end of the range cannot be considered significant clinical or resource utilization outcomes as described within. Although the authors note that the types of rescues activated were collected, the actual distribution by type of event before and after surveillance was not provided in the article. This article does break with tradition in a positive way in that it studies the impact of SpO2 surveillance in a clinical area where, by routine practice, patients are only assessed intermittently and where hypoxic events are not rare. In the past, the value of pulse oximetry was assessed in areas where intensive monitoring was already the rule. One cannot help but be a bit confounded by the results of this study because important data are absent. At its heart, the authors claim that surveillance reduces interventions, but how is this possible? A priori, more monitoring should detect more true hypoxia, which in turn should lead to more interventions (at an earlier stage, perhaps), not fewer, in order to improve true clinical outcomes. Are there important patient care interventions that are excluded from reporting in this article, such as direct nursing care and calls to and action by responsible physicians, among others? No mention is made of what process a floor nurse was to follow, protocol-driven or ad hoc, once notified by the central paging system. Did the frequency of nurse intervention in adjusting a patient’s posture or supplemental oxygen delivery, among other actions, change with surveillance? Finally, one is left curious about the impact of more intense respiratory monitoring on postoperative management and patient satisfaction because these are not addressed in the article. We look forward to further research using this model once adequately powered to discern clinically relevant outcomes. Ira J. Rampil, M.S., M.D.,* Linda S. Rampil, M.S., M.S.W., R.N. *Health Science Center, Stony Brook University, Stony Brook, New York. ira.rampil@sunysb.edu

Where Is the Impact

Human speed perception is contrast dependent

For the study of human and animal vision, the video framebuffer is the only technology that is capable of displaying two-dimensional images with precise control of contrast, luminance, and display timing. The video framebuffer also allows precise control of color. However, this device is not designed for precise psychophysical displays, and techniques must be developed to use them in this role. In order to be concrete, an RDS-3000 raster display system (Adage, 1982) is used which is hosted by a PDP-11/73 under the Venix operating system. The principles generalize to other machines. Where it clarifies the issues, the syntax of the software routines involved is shown.

/pdf/use-of-a-raster-framebuffer-in-vision-research-440uiyezlw.pdf

Use of a raster framebuffer in vision research

Bracewell has proposed the Discrete Hartley Transform (DHT) as a substitute for the Discrete Fourier Transform (DFT), particularly as a means of convolution. Here, it is shown that the most natural extension of the DHT to two dimensions fails to be separate in the two dimensions, and is therefore inefficient. An alternative separable form is considered, corresponding convolution theorem is derived. That the DHT is unlikely to provide faster convolution than the DFT is also discussed.

/pdf/separable-two-dimensional-discrete-hartley-transform-43ufw8qkmi.pdf

Allen Poirson

Papers

Use of a raster framebuffer in vision research

Separable two-dimensional discrete Hartley transform