ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / This paper describes a psychophysical experiment to measure visibility thresholds (VT) for quantization distortion in JPEG2000 and an associated quantization algorithm for visually lossless coding of color aerial images. The visibility thresholds are obtained from a quantization distortion model based on the statistical characteristics of wavelet coefficients and the deadzone quantizer of JPEG2000, and the resulting visibility thresholds are presented for the luminance component (Y) and two chrominance components (Cb and Cr). Using the thresholds, we have achieved visually lossless coding for 24-bit color aerial images at an average bitrate of 4.17 bits/pixels, which is approximately 30% of the bitrate required for numerically lossless coding.

ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California / In this work, a scalable perceptual JPEG2000 encoder that exploits properties of the human visual system (HVS) is presented. The algorithm modifies the final three stages of a conventional JPEG2000 encoder. In the first stage, the quantization step size for each subband is chosen to be the inverse of the contrast sensitivity function (CSF). In bit-plane coding, two masking effects are considered during distortion calculation. In the final bitstream formation step, quality layers are formed corresponding to desired perceptual distortion thresholds. This modified encoder exhibits superior visual performance for remote sensing images compared to conventional JPEG2000 encoders. Additionally, it is completely JPEG2000 Part-1 compliant, and therefore can be decoded by any JPEG2000 decoder.

Florida Department of Transportation (FDOT) has been using Open Graded Friction Course (OGFC) mixture to improve skid resistance of asphalt pavements under wet weather. The OGFC mixture design strongly depends on the Optimum Binder Content (OBC) which represents if the mixture has sufficient bonding between the aggregate and asphalt binder. At present, the FDOT designs OGFC mixtures using a pie plate visual draindown method (FM 5-588). In this method, the OBC is determined based on visual inspection of the asphalt binder draindown (ABD) configuration of three OGFC samples placed on pie plates with pre-determined trial asphalt binder contents (AC). The inspection of the ABD configuration is performed by trained and experienced technicians who determine the OBC using perceptive interpolation or extrapolation based on the known AC of the above samples. In order to eliminate the human subjectivity involved in the current visual method, an automated method for quantifying the OBC of OGFC mixtures was developed using digital images of the pie plates and concepts of perceptual image coding and neural network (NN). Phase I of the project involved the FM-5-588 based OBC testing of OGFC mixture designs consisting of a large set of samples prepared from a variety of granitic and oolitic limestone aggregate sources used by FDOT. Then the digital images of the pie plates containing samples of the above mixtures were acquired using an imaging setup customized by FDOT. The correlation between relevant digital imaging parameters and the corresponding AC was investigated initially using conventional regression analysis. Phase II of the project involved the development of a perceptual image model using human perception metrics considered to be used in the OBC estimation. A General Regression Neural Network (GRNN) was used to uncover the nonlinear correlation between the selected parameters of pie plate images, the corresponding AC and the visually estimated OBC. GRNN was found to be the most viable method to deal with the multi-dimensional nature of the input test data set originating from each individual OGFC sample that contains AC and imaging parameter information from a set of three pie plates. GRNN was trained by 70% and tested by 30% of the database completed in Phase I. Phase III of the project involved the configuration of a quality control tool (QCT) for the aforementioned automated method to enhance its robustness and the likelihood of implementation by other agencies and contractors. QCT is developed using three quality control imaging parameters (QCIP), orientation, spatial distribution, and segregation of ABD configuration of pie plate specimens (PPS) images. Then, the above QCIP were evaluated from PPS images of a variety of independent mixture designs produced using the FDOT visual method. In general, this study found that the newly developed software (GRNN-based) provides satisfactory and reliable estimations of OBC. Furthermore, the statistical and computer-generated results indicated that the selected QCIP are adequate for the formulation of quality control criteria for PPS production. It is believed that the developed QCT will enhance the reliability of the automated OBC estimation image processing-based methodology.