Prioritizing Content of Interest in Multimedia Data Compression

Image and video compression techniques make data transmission and storage in digital multimedia systems more efficient and feasible for the system's limited storage and bandwidth. Many generic image and video compression techniques such as JPEG and H.264/AVC have been standardized and are now widely adopted. Despite their great success, we observe that these standard compression techniques are not the best solution for data compression in special types of multimedia systems such as microscopy videos and low-power wireless broadcast systems. In these application-specific systems where the content of interest in the multimedia data is known and well-defined, we should re-think the design of a data compression pipeline. We hypothesize that by identifying and prioritizing multimedia data's content of interest, new compression methods can be invented that are far more effective than standard techniques. In this dissertation, a set of new data compression methods based on the idea of prioritizing the content of interest has been proposed for three different kinds of multimedia systems. I will show that the key to designing efficient compression techniques in these three cases is to prioritize the content of interest in the data. The definition of the content of interest of multimedia data depends on the application. First, I show that for microscopy videos, the content of interest is defined as the spatial regions in the video frame with pixels that don't only contain noise. Keeping data in those regions with high quality and throwing out other information yields to a novel microscopy video compression technique. Second, I show that for a Bluetooth low energy beacon based system, practical multimedia data storage and transmission is possible by prioritizing content of interest. I designed custom image compression techniques that preserve edges in a binary image, or foreground regions of a color image of indoor or outdoor objects. Last, I present a new indoor Bluetooth low energy beacon based augmented reality system that integrates a 3D moving object compression method that prioritizes the content of interest.Doctor of Philosoph

A Neutral Network Based Vehicle Classification System for Pervasive Smart Road Security

Pervasive smart computing environments make people get accustomed to convenient and secure services. The overall goal of this research is to classify vehicles along the I215 freeway in Salt Lake City, USA. This information will be used to predict future roadway needs and the expected life of a roadway. The classification of vehicles will be performed by a synthesis of multiple sets of features. All feature sets have not yet been determined; however, one such set will be the reduced wavelet transform of the image of a vehicle. In order to use such a feature, it is necessary that the image be normalized with respect to size, position, and so on. For example, a car in the right most lane in an image will appear smaller than one in the left most lane, because the right most lane is closest to the camera. Likewise, a vehicle’s size will vary depending on where in a lane its image is captured. In our case, the image capture area for each lane is approximately 100 feet of roadway. A goal of this paper is to normalize the image of a vehicle so that regardless of its lane or position in a lane, the features will be approximately the same. The wavelet transform itself will not be used directly for recognition. Instead, it will be input to a neural network and the output of the neural network will be one element of the feature set used for recognition

A computational visual saliency model for images.

Human eyes receive an enormous amount of information from the visual world. It is highly difficult to simultaneously process this excessive information for the human brain. Hence the human visual system will selectively process the incoming information by attending only the relevant regions of interest in a scene. Visual saliency characterises some parts of a scene that appears to stand out from its neighbouring regions and attracts the human gaze. Modelling saliency-based visual attention has been an active research area in recent years. Saliency models have found vital importance in many areas of computer vision tasks such as image and video compression, object segmentation, target tracking, remote sensing and robotics. Many of these applications deal with high-resolution images and real-time videos and it is a challenge to process this excessive amount of information with limited computational resources. Employing saliency models in these applications will limit the processing of irrelevant information and further will improve their efficiency and performance. Therefore, a saliency model with good prediction accuracy and low computation time is highly essential. This thesis presents a low-computation wavelet-based visual saliency model designed to predict the regions of human eye fixations in images. The proposed model uses two-channel information luminance (Y) and chrominance (Cr) in YCbCr colour space for saliency computation. These two channels are decomposed to their lowest resolution using two-dimensional Discrete Wavelet Transform (DWT) to extract the local contrast features at multiple scales. The extracted local contrast features are integrated at multiple levels using a two-dimensional entropy-based feature combination scheme to derive a combined map. The combined map is normalized and enhanced using natural logarithm transformation to derive a final saliency map. The performance of the model has been evaluated qualitatively and quantitatively using two large benchmark image datasets. The experimental results show that the proposed model has achieved better prediction accuracy both qualitatively and quantitatively with a significant reduction in computation time when compared to the existing benchmark models. It has achieved nearly 25% computational savings when compared to the benchmark model with the lowest computation time

Scalable Speech Coding for IP Networks

The emergence of Voice over Internet Protocol (VoIP) has posed new challenges to the development of speech codecs. The key issue of transporting real-time voice packet over IP networks is the lack of guarantee for reasonable speech quality due to packet delay or loss. Most of the widely used narrowband codecs depend on the Code Excited Linear Prediction (CELP) coding technique. The CELP technique utilizes the long-term prediction across the frame boundaries and therefore causes error propagation in the case of packet loss and need to transmit redundant information in order to mitigate the problem. The internet Low Bit-rate Codec (iLBC) employs the frame-independent coding and therefore inherently possesses high robustness to packet loss. However, the original iLBC lacks in some of the key features of speech codecs for IP networks: Rate flexibility, Scalability, and Wideband support. This dissertation presents novel scalable narrowband and wideband speech codecs for IP networks using the frame independent coding scheme based on the iLBC. The rate flexibility is added to the iLBC by employing the discrete cosine transform (DCT) and iii the scalable algebraic vector quantization (AVQ) and by allocating different number of bits to the AVQ. The bit-rate scalability is obtained by adding the enhancement layer to the core layer of the multi-rate iLBC. The enhancement layer encodes the weighted iLBC coding error in the modified DCT (MDCT) domain. The proposed wideband codec employs the bandwidth extension technique to extend the capabilities of existing narrowband codecs to provide wideband coding functionality. The wavelet transform is also used to further enhance the performance of the proposed codec. The performance evaluation results show that the proposed codec provides high robustness to packet loss and achieves equivalent or higher speech quality than state-of-the-art codecs under the clean channel condition

QUALITY-DRIVEN CROSS LAYER DESIGN FOR MULTIMEDIA SECURITY OVER RESOURCE CONSTRAINED WIRELESS SENSOR NETWORKS

The strong need for security guarantee, e.g., integrity and authenticity, as well as privacy and confidentiality in wireless multimedia services has driven the development of an emerging research area in low cost Wireless Multimedia Sensor Networks (WMSNs). Unfortunately, those conventional encryption and authentication techniques cannot be applied directly to WMSNs due to inborn challenges such as extremely limited energy, computing and bandwidth resources. This dissertation provides a quality-driven security design and resource allocation framework for WMSNs. The contribution of this dissertation bridges the inter-disciplinary research gap between high layer multimedia signal processing and low layer computer networking. It formulates the generic problem of quality-driven multimedia resource allocation in WMSNs and proposes a cross layer solution. The fundamental methodologies of multimedia selective encryption and stream authentication, and their application to digital image or video compression standards are presented. New multimedia selective encryption and stream authentication schemes are proposed at application layer, which significantly reduces encryption/authentication complexity. In addition, network resource allocation methodologies at low layers are extensively studied. An unequal error protection-based network resource allocation scheme is proposed to achieve the best effort media quality with integrity and energy efficiency guarantee. Performance evaluation results show that this cross layer framework achieves considerable energy-quality-security gain by jointly designing multimedia selective encryption/multimedia stream authentication and communication resource allocation

Layer-based coding, smoothing, and scheduling of low-bit-rate video for teleconferencing over tactical ATM networks

This work investigates issues related to distribution of low bit rate video within the context of a teleconferencing application deployed over a tactical ATM network. The main objective is to develop mechanisms that support transmission of low bit rate video streams as a series of scalable layers that progressively improve quality. The hierarchical nature of the layered video stream is actively exploited along the transmission path from the sender to the recipients to facilitate transmission. A new layered coder design tailored to video teleconferencing in the tactical environment is proposed. Macroblocks selected due to scene motion are layered via subband decomposition using the fast Haar transform. A generalized layering scheme groups the subbands to form an arbitrary number of layers. As a layering scheme suitable for low motion video is unsuitable for static slides, the coder adapts the layering scheme to the video content. A suboptimal rate control mechanism that reduces the kappa dimensional rate distortion problem resulting from the use of multiple quantizers tailored to each layer to a 1 dimensional problem by creating a single rate distortion curve for the coder in terms of a suboptimal set of kappa dimensional quantizer vectors is investigated. Rate control is thus simplified into a table lookup of a codebook containing the suboptimal quantizer vectors. The rate controller is ideal for real time video and limits fluctuations in the bit stream with no corresponding visible fluctuations in perceptual quality. A traffic smoother prior to network entry is developed to increase queuing and scheduler efficiency. Three levels of smoothing are studied: frame, layer, and cell interarrival. Frame level smoothing occurs via rate control at the application. Interleaving and cell interarrival smoothing are accomplished using a leaky bucket mechanism inserted prior to the adaptation layer or within the adaptation layerhttp://www.archive.org/details/layerbasedcoding00parkLieutenant Commander, United States NavyApproved for public release; distribution is unlimited