Engineering topics

Solomon, Joshua A.

Publications and source records attributed to Solomon, Joshua A..

5 records

NASA NTRS1996

Perceptually Lossless Wavelet Compression

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 is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter, which we call DWT uniform quantization noise. 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 r 2(exp -1), where r is display visual resolution in pixels/degree, and L is the wavelet level. Amplitude thresholds increase rapidly with spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from low-pass to horizontal/vertical to diagonal. We propose 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.

Watson, Andrew B.↗

NASA NTRS1995

Visibility of Wavelet Quantization Noise

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 is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter, which we call DWT uniform quantization noise. 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 r 2(exp)-L , where r is display visual resolution in pixels/degree, and L is the wavelet level. Amplitude thresholds increase rapidly with spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from low-pass to horizontal/vertical to diagonal. We describe a mathematical model to predict DWT noise detection thresholds as 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.

Watson, Andrew B.↗

NASA NTRS1995

Spatial and Spatial Frequency Spreads of Masking: Measurements and A Contrast-Gain-Control Model

Masked pattern detection is affected by the mask's proximity to the target in both the spatial and the spatial frequency domains. We measured contrast thresholds for detection of a Gabor patch centered in a sample of static band-pass noise with a central noise-free aperture. To examine spatial spread, target and mask frequency were identical, at either 2, 4, or 8 cycles/degree (cpd), and the radius of the aperture was 0, 1/2 or 1 cycle of the noise band's center frequency. The results suggest that the spatial spread of masking is scale invariant, with the largest radius producing little masking. To examine spatial frequency spread, a radius of 0 (no aperture) was used with all pairings of 2, 4, and 8 cpd for target and mask. The results suggest that masking is asymmetrical over log frequency: 8 z 0 cpd noise masks a 2 cpd target, but 2 cpd noise does not mask an 8 cpd target. We have used these results to calibrate a model of contrast-gain-control.

Solomon, Joshua A.↗

NASA NTRS1994

Visibility of DCT Quantization Error: Effects of Display Resolution

As part of a program of research to understand the visibility of DCT quantization errors and thereby design optimal quantizers, we measured visibility of DCT quantization error as a function of display resolution in pixels/degree. Visibilities are consistent with a model incorporating effects of block size and spatial pooling.

Watson, Andrew B.↗

NASA NTRS1994

Contrast Gain Control Model Fits Masking Data

We studied the fit of a contrast gain control model to data of Foley (JOSA 1994), consisting of thresholds for a Gabor patch masked by gratings of various orientations, or by compounds of two orientations. Our general model includes models of Foley and Teo & Heeger (IEEE 1994). Our specific model used a bank of Gabor filters with octave bandwidths at 8 orientations. Excitatory and inhibitory nonlinearities were power functions with exponents of 2.4 and 2. Inhibitory pooling was broad in orientation, but narrow in spatial frequency and space. Minkowski pooling used an exponent of 4. All of the data for observer KMF were well fit by the model. We have developed a contrast gain control model that fits masking data. Unlike Foley's, our model accepts images as inputs. Unlike Teo & Heeger's, our model did not require multiple channels for different dynamic ranges.

Watson, Andrew B.↗