Optimal polar image sampling

Abstract

In this paper, a problem of efficient image sampling (deployment of image sensors) is considered. This problem is solved using techniques of two-dimensional quantization in polar coordinates, taking into account human visual system (HVS) and eye sensitivity function. The optimal radial compression function for polar quantization is derived. Optimization of the number of the phase levels for each amplitude level is done. Using optimal radial compression function and optimal number of phase levels for each amplitude level, optimal polar quantization is defined. Using deployment of quantization cells for the optimal polar quantization, deployment of image sensors is done, and therefore optimal polar image sampling is obtained. It is shown that our solution (the optimal polar sampling) has many advantages compared to presently used solutions, based on the log-polar sampling. The optimal polar sampling gives higher SNR (signal-to-noise ratio), compared to the log-polar sampling, for the same number of sensors. Also, the optimal polar sampling needs smaller number of sensors, to achieve the same SNR, compared to the log-polar sampling. Furthermore, with the optimal polar sampling, points in the image middle can be sampled, which is not valid for the log-polar sampling. This is very important since human eye is the most sensitive to these points, and therefore the optimal polar sampling gives better subjective quality.

Designing quantizers for coding signals with fixed and variable codeword length

“…The main drawback of the log-polar sampling is the fact that compression function is not defined for small r ( We propose the optimal image sampling [27], where deployment of sensors is achieved according to the cells deployment in the polar quantizer with the optimal compression function [28] The first advantage is that there is no black point in the image middle. Also, the optimal image sampling has much better performances than the log-polar sampling, since for the same number of sensors it gives much higher values of SNR, which can be seen in Fig.…”