Ali M. Dawood

Content-based MPEG video traffic modeling

In this paper, we propose a video model to generate VBR MPEG video traffic based on the scene content description. Long sessions of nonhomogeneous video clips are decomposed into homogeneous video shots. The shots are then classified into different classes in terms of their texture and motion complexity. Each shot class was uniquely described with an autoregressive model. Transitions between the shots and their durations have been analyzed. Unlike many classical video source models, this model may be used to generate traffic of any type of video scenes ranging from a low complexity video conferencing to a highly active sport program. The performance of the model is evaluated by measuring the mean cell delay when the generated video traffic is fed to an ATM multiplex buffer.

Scene content classification from MPEG coded bit streams

We propose a classification method of MPEG coded video based on the image texture and motion levels of the scene content. The texture and motion are divided into three levels each, low, medium and high, resulting into nine scene categories. Since in MPEG-1 (and MPEG-2) encoder the DCT coefficients of the I-frame are affected by the image texture, while the motion affects the macroblocks coding mode of P- and B-frames, hence, an automatic scene classification can be obtained from the coded bit stream based on the scenes texture and motion levels. The method is useful for the content-based video analysis, such as video modeling and MPEG-7.

MPEG video modelling based on scene description

We propose a video model to generate a VBR MPEG video traffic based on the scene description. Long sessions of non-homogeneous video clips are decomposed into homogeneous video shots. The shots are then classified into different classes in terms of their texture and motion complexity. Each shot class can be uniquely described and modelled as a homogeneous video. The model may be used to generate traffic of any type of video scenes ranging from a low complexity video conferencing to a highly active sport program.

Global mapping of height of bright band

Radar observations of the melting layer of precipitation have been made since the dawn of radar meteorology. It has been known since then that the melting of precipitation is often associated with an enhancement of the reflectivity of weather targets. The primary cause of the enhancement is a rapid increase in the dielectric constant of hydrometeors at the top of the melting layer followed by an increase of the velocities of melting snowflakes toward the end of the melting process [3]. The radar bright band results mostly from melting of snowflakes as they fall through the 0 degC isotherm. As the ice is gradually transformed to liquid, the refractive index and, hence, the back-scattering cross section increase, and the radar echo intensity increases to a maximum at ap 200 m below the 0degC isotherm. The decrease of echo intensity below the bright band level is mostly the result of decreases of particle concentration caused by increases of terminal velocity as the particles melt. 2A23 TRMM data provides information about the height of bright band (HBB) as well as the height of freezing. HBB is generally close to the height of freezing (usually within plusmn 2 km). In this paper, data from the TRMM PR are used to generate monthly maps of the height of bright band (HBB over the globe for the year 2000, 3 dB thickness of BB is developed, HBB over ocean and land and during some events such as hurricane are examined.

Intelligent technique for scene cut detection from MPEG video

In this paper we propose a neuro-fuzzy (NF) algorithm that automatically detects the type of MPEG frame at which a clean scene cut occurred. The algorithm is based on detection of the majority of intra-macroblocks and directionality of the two neighboring B-frame motion vector types from the bit stream, it is shown that the method is able to identify the location of the scene cut I-frame, P-frame and each B-frame with a very high success rate.

An Optimized Selective Encryption for Video Confidentiality

Selective Encryption (SE) offers effective and efficient protection of visual content for assuring video confidentiality. However, it is challenging to optimize SE’s computational overhead while maintaining encryption effectiveness. This paper suggests the study of classifying the targeted sign-flip Quantized Coefficients (QC) so as to minimize the Encryption Computation Overhead (ECO), while maximizing Perceptual Encryption Effectiveness (PEE) both across the H.264/AVC and the HEVC video coding standards. The evaluation of this min-max optimization is based on the number of encryption bits used to flip the targeted QC sign and the Peak Signal to Noise Ratio (PSNR) estimated post encryption for quantifying ECO and PEE respectively. Results of simulation on different video sequences indicate that the derived set of QC’s can reduce ECO cost by up to 78 % without affecting the video confidentiality when compared to state-of-the-art approaches.

Using remote sensing satellites for water quality monitoring in the UAE

The GCC (Gulf Cooperation Council) countries lack adequate freshwater availability. Hence, most of the fresh water is obtained from seawater desalination. Because seawater is the main source of fresh water and can be polluted by human and non human activities, it needs to be continuously protected and monitored. The objective of this paper is to highlight the development of an automated approach for monitoring the water quality in the UAE. Visible and thermal measurements provided by MODIS (MODerate-resolution Imaging Spectro-radiometer) sensors on board of Terra and Aqua satellites are used in this project. Medium resolution data of MODIS will be complemented with very high resolution data from the newly launched satellite DubaiSat-1 (DS-1). The project deals with monitoring of pollution caused by desalination plants, red-tide and oil spills.

Characteristics and properties of United Arab Emirates fog events using meteosat second generation thermal and visible channels

Satellite remote sensing is an important tool in the detection and short range forecasting (now-casting) of fog events. Fog over land develops primarily during the late-night and pre-dawn hours, infrared remote sensing is indispensable in observing fog formation, while visible imagery helps to monitor the extent and density of fog after sunrise. On average there are more than forty five fog occurrences in UAE per year. This paper describes the methods used by EIAST for using satellite remote sensing for detecting and now-casting fog events. The temperature difference between two infrared bands (11 μm and 4 μm) forms the basis for fog detection and classification [1].

Image Fusion Tool for Dubai Sat-1 images

In this paper, an Image Fusion Tool for satellite images is proposed. It is mainly developed to enhance quality of images taken from DubaiSat-1 remote sensing satellite. Two different methods were proposed, one is called Pixel-intensity Fusion (PIF) and the other is Transferred color space Fusion (TCSF). Each technique is presented with two options. Each method is also consisting two approaches. Results show that TCSF is superior to the two PIF method.

Using medium and high resolution satellite images in monitoring water quality surrounding the discharges of desalination plants in the UAE

Today, various processes of seawater desalination are used to satisfy the increasing demand of fresh water. Desalination plants treat the seawater pumped from their intakes and discharge the generated waste back to the sea through their outfalls. The discharged waste, also called brine, has a high salinity level, high temperature and high concentration of other suspended matters which may cause negative environmental effects on the water quality surrounding the discharge point. In this paper, medium and high resolution satellite data collected by MODIS and DubaiSat-1 (DS-1), respectively, were used to derive average digital value and its spatial variability surrounding Jebel Ali plant. Satellite-derived sea surface temperature (SST) was derived and compared to field measured temperature collected between January and December 2008. In order to have a better understanding of the spectral and spatial characteristics of satellite data, the mean squared error (MSE) and peak signal to noise ratio (PSNR) were also calculated for different locations in the study area with determined windows size. Structural Similarity (SSIM) factor was used to compare two locations, one with pollution and the other without pollution.