Area Optimized H.264 Intra Prediction Architecture for 1080p HD Resolution

High performance video standards use prediction techniques to achieve high picture quality at low bit rates. The type of prediction decides the bit rates and the image quality. Intra Prediction achieves high video quality with significant reduction in bit rate. This paper present an area optimized architecture for Intra prediction, for H.264 decoding at HDTV resolution with a target of achieving 60 fps. The architecture was validated on Virtex-5 FPGA based platform. The architecture achieves a frame rate of 64 fps. The architecture is based on multi-level memory hierarchy to reduce latency and ensure optimum resources utilization. It removes redundancy by reusing same functional blocks across different modes. The proposed architecture uses only 13% of the total LUTs available on the Xilinx FPGA XC5VLX50T.

HD resolution intra prediction architecture for H.264 decoder

High performance video standards use prediction techniques to achieve high picture quality at low bit rates. The type of prediction decides the bit rates and the image quality. Intra Prediction achieves high video quality with significant reduction in bit rate. This paper presents novel area optimized architecture for Intra prediction of H.264 decoding at HDTV resolution. The architecture has been validated on a Xilinx Virtex-5 FPGA based platform and achieved a frame rate of 64 fps. The architecture is based on multi-level memory hierarchy to reduce latency and ensure optimum resources utilization. It removes redundancy by reusing same functional blocks across different modes. The proposed architecture uses only 13% of the total LUTs available on the Xilinx FPGA XC5VLX50T.

THE IMPROVED SIGN BIT ENCRYPTION OF MOTION VECTORS FOR H.264/AVC

In this paper, an improved video encryption method for encrypting the sign bit of motion vectors is proposed based on H.264/AVC, which belongs to selective encryption. This method improves upon previous work involving the sign bit encryption of motion vectors by ensuring the four candidates for the encrypted motion vectors are always located in two orthogonal lines. The improved method can provide a much more effective scrambling effect while keeping the encrypted stream format-compliant and the compression ratio unchanged. The combination of the proposed method with encryption of intra prediction modes can further enhance the scrambling effect, especially for the first few frames which are left clear when only the motion vectors are encrypted.

A Tunable Encryption Scheme and Analysis of Fast Selective Encryption for CAVLC and CABAC in H.264/AVC

Recently, two fast selective encryption methods for context-adaptive variable length coding and context-adaptive binary arithmetic coding in H.264/AVC were proposed by Shahid et al. In this paper, it was demonstrated that these two methods are not as efficient as only encrypting the sign bits of nonzero coefficients. Experimental results showed that without encrypting the sign bits of nonzero coefficients, these two methods can not provide a perceptual scrambling effect. If a much stronger scrambling effect is required, intra prediction modes, and the sign bits of motion vectors can be encrypted together with the sign bits of nonzero coefficients. For practical applications, the required encryption scheme should be customized according to a user's specified requirement on the perceptual scrambling effect and the computational cost. Thus, a tunable encryption scheme combining these three methods is proposed for H.264/AVC. To simplify its implementation and reduce the computational cost, a simple control mechanism is proposed to adjust the control factors. Experimental results show that this scheme can provide different scrambling levels by adjusting three control factors with no or very little impact on the compression performance. The proposed scheme can run in real-time and its computational cost is minimal. The security of the proposed scheme is also discussed. It is secure against the replacement attack when all three control factors are set to one.

Privacy Region Protection for H.264/AVC with Enhanced Scrambling Effect and a Low Bitrate Overhead

While video surveillance systems have become ubiquitous in our daily lives, they have introduced concerns over privacy invasion. Recent research to address these privacy issues includes a focus on privacy region protection, whereby existing video scrambling techniques are applied to specific regions of interest (ROI) in a video while the background is left unchanged. Most previous work in this area has only focussed on encrypting the sign bits of nonzero coefficients in the privacy region, which produces a relatively weak scrambling effect. In this paper, to enhance the scrambling effect for privacy protection, it is proposed to encrypt the intra prediction modes (IPM) in addition to the sign bits of nonzero coefficients (SNC) within the privacy region. A major issue with utilising encryption of IPM is that drift error is introduced outside the region of interest. Therefore, a re-encoding method, which is integrated with the encryption of IPM, is also proposed to remove drift error. Compared with a previous technique that uses encryption of IPM, the proposed re-encoding method offers savings in the bitrate overhead while completely removing the drift error. Experimental results and analysis based on H.264/AVC were carried out to verify the effectiveness of the proposed methods. In addition, a spiral binary mask mechanism is proposed that can reduce the bitrate overhead incurred by flagging the position of the privacy region. A definition of the syntax structure for the spiral binary mask is given. As a result of the proposed techniques, the privacy regions in a video sequence can be effectively protected by the enhanced scrambling effect with no drift error and a lower bitrate overhead.

Improved Techniques for Video Compression and Communication

Thesis (Ph.D.)--University of Washington, 2016-06

Prediction of postprandial blood glucose under intra-patient variability and uncertainty and its use in the design of insulin dosing strategies for type 1 diabetic patients

In this thesis I propose a novel method to estimate the dose and injection-to-meal time for low-risk intensive insulin therapy. This dosage-aid system uses an optimization algorithm to determine the insulin dose and injection-to-meal time that minimizes the risk of postprandial hyper- and hypoglycaemia in type 1 diabetic patients. To this end, the algorithm applies a methodology that quantifies the risk of experiencing different grades of hypo- or hyperglycaemia in the postprandial state induced by insulin therapy according to an individual patient’s parameters. This methodology is based on modal interval analysis (MIA). Applying MIA, the postprandial glucose level is predicted with consideration of intra-patient variability and other sources of uncertainty. A worst-case approach is then used to calculate the risk index. In this way, a safer prediction of possible hyper- and hypoglycaemic episodes induced by the insulin therapy tested can be calculated in terms of these uncertainties.

The use of body circumferences for the prediction of intra-abdominal fat in obese women with polycystic ovary syndrome

Introduction: Computerizd tomography (CT) is the gold standard for the evaluation of intra- (IAF) and total (TAF) abdominal fat; however, the high cost of the procedure and exposure to radiation limit its routine use. Objective: To develop equations that utilize anthropometric measures for the estimate of IAF and TAF in obese women with polycystic ovary syndrome (PCOS). Methods: The weight, height, BMI, and abdominal (AC), waist (WC), chest (CC), and neck (NC) circumferences of thirty obese women with PCOS were measured, and their IAF and TAF were analyzed by CT. Results: The anthropometric variables AC, CC, and NC were chosen for the TAF linear regression model because they were better correlated with the fat deposited in this region. The model proposed for TAF (predicted) was: 4.63725 + 0.01483 x AC - 0.00117 x NC - 0.00177 x CC (R-2 = 0.78); and the model proposed for IAF was: IAF (predicted) = 1.88541 + 0.01878 x WC + 0.05687 x NC - 0.01529 x CC (R-2 = 0.51). AC was the only independent predictor of TAF (p < 0.01). Conclusion: The equations proposed showed good correlation with the real value measured by CT, and can be used in clinical practice. (Nutr Hosp. 2012;27:1662-1666) DOI:10.3305/nh.2012.27.5.5933

Intra- and interlaboratory reliability of a cryopreserved trout hepatocyte assay for the prediction of chemical bioaccumulation potential

Measured rates of intrinsic clearance determined using cryopreserved trout hepatocytes can be extrapolated to the whole animal as a means of improving modeled bioaccumulation predictions for fish. To date, however, the intra- and interlaboratory reliability of this procedure has not been determined. In the present study, three laboratories determined in vitro intrinsic clearance of six reference compounds (benzo[a]pyrene, 4-nonylphenol, di-tert-butyl phenol, fenthion, methoxychlor and o-terphenyl) by conducting substrate depletion experiments with cryopreserved trout hepatocytes from a single source. O-terphenyl was excluded from the final analysis due to nonfirst-order depletion kinetics and significant loss from denatured controls. For the other five compounds, intralaboratory variability (% CV) in measured in vitro intrinsic clearance values ranged from 4.1 to 30%, while interlaboratory variability ranged from 27 to 61%. Predicted bioconcentration factors based on in vitro clearance values exhibited a reduced level of interlaboratory variability (5.3-38% CV). The results of this study demonstrate that cryopreserved trout hepatocytes can be used to reliably obtain in vitro intrinsic clearance of xenobiotics, which provides support for the application of this in vitro method in a weight-of-evidence approach to chemical bioaccumulation assessment.

Non-destructive prediction of 'Hass' avocado dry matter via FT-NIR spectroscopy.

BACKGROUND: The inability to consistently guarantee internal quality of horticulture produce is of major importance to the primary producer, marketers and ultimately the consumer. Currently, commercial avocado maturity estimation is based on the destructive assessment of percentage dry matter (%DM), and sometimes percentage oil, both of which are highly correlated with maturity. In this study the utility of Fourier transform (FT) near-infrared spectroscopy (NIRS) was investigated for the first time as a non-invasive technique for estimating %DM of whole intact 'Hass' avocado fruit. Partial least squares regression models were developed from the diffuse reflectance spectra to predict %DM, taking into account effects of intra-seasonal variation and orchard conditions. RESULTS: It was found that combining three harvests (early, mid and late) from a single farm in the major production district of central Queensland yielded a predictive model for %DM with a coefficient of determination for the validation set of 0.76 and a root mean square error of prediction of 1.53% for DM in the range 19.4-34.2%. CONCLUSION: The results of the study indicate the potential of FT-NIRS in diffuse reflectance mode to non-invasively predict %DM of whole 'Hass' avocado fruit. When the FT-NIRS system was assessed on whole avocados, the results compared favourably against data from other NIRS systems identified in the literature that have been used in research applications on avocados.

Comparison of Prediction Based LSF Quantization Methods using Split VQ

Further improvement in performance, to achieve near transparent quality LSF quantization, is shown to be possible by using a higher order two dimensional (2-D) prediction in the coefficient domain. The prediction is performed in a closed-loop manner so that the LSF reconstruction error is the same as the quantization error of the prediction residual. We show that an optimum 2-D predictor, exploiting both inter-frame and intra-frame correlations, performs better than existing predictive methods. Computationally efficient split vector quantization technique is used to implement the proposed 2-D prediction based method. We show further improvement in performance by using weighted Euclidean distance.

Essays on the Modeling and Prediction of Volatility and Higher Moments of Stock Returns (summary section only)

One of the most fundamental and widely accepted ideas in finance is that investors are compensated through higher returns for taking on non-diversifiable risk. Hence the quantification, modeling and prediction of risk have been, and still are one of the most prolific research areas in financial economics. It was recognized early on that there are predictable patterns in the variance of speculative prices. Later research has shown that there may also be systematic variation in the skewness and kurtosis of financial returns. Lacking in the literature so far, is an out-of-sample forecast evaluation of the potential benefits of these new more complicated models with time-varying higher moments. Such an evaluation is the topic of this dissertation. Essay 1 investigates the forecast performance of the GARCH (1,1) model when estimated with 9 different error distributions on Standard and Poor’s 500 Index Future returns. By utilizing the theory of realized variance to construct an appropriate ex post measure of variance from intra-day data it is shown that allowing for a leptokurtic error distribution leads to significant improvements in variance forecasts compared to using the normal distribution. This result holds for daily, weekly as well as monthly forecast horizons. It is also found that allowing for skewness and time variation in the higher moments of the distribution does not further improve forecasts. In Essay 2, by using 20 years of daily Standard and Poor 500 index returns, it is found that density forecasts are much improved by allowing for constant excess kurtosis but not improved by allowing for skewness. By allowing the kurtosis and skewness to be time varying the density forecasts are not further improved but on the contrary made slightly worse. In Essay 3 a new model incorporating conditional variance, skewness and kurtosis based on the Normal Inverse Gaussian (NIG) distribution is proposed. The new model and two previously used NIG models are evaluated by their Value at Risk (VaR) forecasts on a long series of daily Standard and Poor’s 500 returns. The results show that only the new model produces satisfactory VaR forecasts for both 1% and 5% VaR Taken together the results of the thesis show that kurtosis appears not to exhibit predictable time variation, whereas there is found some predictability in the skewness. However, the dynamic properties of the skewness are not completely captured by any of the models.

Northeast monsoon over India: variability and prediction

South peninsular India experiences a large portion of the annual rainfall during the northeast monsoon season (October to December). In this study, the facets of diurnal, intra-seasonal and inter-annual variability of the northeast monsoon rainfall (the NEMR) over India have been examined. The analysis of satellite derived hourly rainfall reveals that there are distinct features of diurnal variation over the land and oceans during the season. Over the land, rainfall peaks during the late afternoon/evening, while over the oceans an early morning peak is observed. The harmonic analysis of hourly data reveals that the amplitude and variance are the largest over south peninsular India. The NEMR also exhibits significant intra-seasonal variability on a 20-40 day time scale. Analysis also shows significant northward propagation of the maximum cloud zone from south of equator to the south peninsula during the season. The NEMR exhibits large inter-annual variability with the co-efficient of variation (CV) of 25%. The positive phases of ENSO and the Indian Ocean Dipole (IOD) are conducive for normal to above normal rainfall activity during the northeast monsoon. There are multi-decadal variations in the statistical relationship between ENSO and the NEMR. During the period 2001-2010 the statistical relationship between ENSO and the NEMR has significantly weakened. The analysis of seasonal rainfall hindcasts for the period 1960-2005 produced by the state-of-the-art coupled climate models, ENSEMBLES, reveals that the coupled models have very poor skill in predicting the inter-annual variability of the NEMR. This is mainly due to the inability of the ENSEMBLES models to simulate the positive relationship between ENSO and the NEMR correctly. Copyright (C) 2012 Royal Meteorological Society