We presentPiCam(Pelican Imaging Camera-Array), an ultra-thin high performance monolithic camera array, that captures light fields and synthesizes high resolution images along with a range image (scene depth) through integrated parallax detection and superresolution. The camera is passive, supporting both stills and video, low light capable, and small enough to be included in the next generation of mobile devices including smartphones. Prior works [Rander et al. 1997; Yang et al. 2002; Zhang and Chen 2004; Tanida et al. 2001; Tanida et al. 2003; Duparré et al. 2004] in camera arrays have explored multiple facets of light field capture - from viewpoint synthesis, synthetic refocus, computing range images, high speed video, and micro-optical aspects of system miniaturization. However, none of these have addressed the modifications needed to achieve the strict form factor and image quality required to make array cameras practical for mobile devices. In our approach, we customize many aspects of the camera array including lenses, pixels, sensors, and software algorithms to achieve imaging performance and form factor comparable to existing mobile phone cameras. Our contributions to the post-processing of images from camera arrays include a cost function for parallax detection that integrates across multiple color channels, and a regularized image restoration (superresolution) process that takes into account all the system degradations and adapts to a range of practical imaging conditions. The registration uncertainty from the parallax detection process is integrated into a Maximum-a-Posteriori formulation that synthesizes an estimate of the high resolution image and scene depth. We conclude with some examples of our array capabilities such as postcapture (still) refocus, video refocus, view synthesis to demonstrate motion parallax, 3D range images, and briefly address future work.

The use of block transforms for coding intra-frames in video coding may preclude higher coding performance due to residual correlation across block boundaries and insufficient energy compaction, which translates into unrealized rate-distortion gains. Subjectively, the occurrence of blocking artifacts is common. Post-filters and lapped transforms offer good solutions to these problems. Lapped transforms offer a more general framework which can incorporate coordinated pre- and post-filtering operations. Most common are fixed lapped transforms (such as lapped orthogonal transforms), and also transforms with adaptive basis function length. In contrast, in this paper we determine a lapped transform that non-linearly adapts its basis functions to local image statistics and the quantization regime. This transform was incorporated into the H.264/AVC codec, and its performance evaluated. As a result, significant rate-distortion gains of up to 0.45 dB (average 0.35dB) PSNR were obtained compared to the H.264/AVC codec alone

We present an empirical registration model derived from the WLAN registration patterns of the mobile users. There exist models that accurately describe individually the spatial and temporal aspects of user registration, and demonstrate the importance of this modeling. The main distinction of the new model from the previous empirical models is that we are able to formulate the inter-dependence of space and time explicitly by a set of few equations. Our extensive studies of the WLAN traces indicate that a simple but proper notion of popularity radient suffices to capture the correlation across space and time. Indeed, when locations (i.e., AP coverage area) are differentiated with respect to the number of visits they are receiving (i.e., AP popularity), the time spent at each location i before user moves from i to k turns out to be closely related to the difference of popularity between locations i and k This observation led to the design of a joint time-space registration model (referred to as ModelT++) that builds upon the Model T, which itself models only the space aspect of the registration, but is derived from the same campus WiFi network. As part of the process of generating a joint space-time model, we further extend spatial aspects of the Model T. We evaluate our model using various metrics against a random walk model as well as the Model T by superimposing location independent time series on these space-only registration models. Our results suggest that with a slight increase in the model complexity, our joint time-space registration model is able to better capture the real network registration than the independent time models. Model T++ can be easily integrated into both WLAN and multi-hop wireless mesh network simulations that require realistic registration models.

We derive an empirical model for spatial registration patterns of mobile users as they move within a campus wireless local area network (WLAN) environment and register at different access points. Such a model can be very useful in a variety of simulation studies of the performance of mobile wireless systems, such as that of resource management and mobility management protocols. We base the model on extensive experimental data from a campus WiFi LAN installation, representing traces from about 6000 users over a period of about 2 years. We divide the empirical data available to us into training and test data sets, develop the model based on the training set, and evaluate it against the test set.The model shows that user registration patterns exhibit a distinct hierarchy, and that WLAN access points (APs) can be clustered based on registration patterns. Cluster size distributions are highly skewed, as are intra-cluster transition probabilities and trace lengths, which can all be modeled well by the heavy-tailed Weibull distribution. The fraction of popular APs in a cluster, as a function of cluster size, can be modeled by exponential distributions. There is general similarity across hierarchies, in that inter-cluster registration patterns tend to have the same characteristics and distributions as intra-cluster patterns.We generate synthetic traces for intra-cluster transitions, inter-cluster transitions, and complete traces, and compare them against the corresponding traces from the test set. We define a set of metrics that evaluate how well the model captures the empirical features it is trying to represent. We find that the synthetic traces agree very well with the test set in terms of the metrics. We also compare the model to a simple modified random waypoint model as a baseline, and show the latter is not at all representative of the real data.The user of the model has the opportunity to use it as is, or can modify model parameters, such as the degree of randomness in registration patterns. We close with a brief discussion of further work to refine and extend the model.

Transform coding plays a central role in image and video coding technologies. Various transforms, whether fixed or adaptive, have been utilized for video compression. However, these transforms are inherently sub-optimal in a coding-efficiency sense, given that their design does not explicitly take into account the entire transform coding model. Thus, commonly used transforms such as the DCT have desirable properties, but were designed without the full consideration of the other transform coding elements including quantization and entropy coding. Although these transforms have good performance in a rate-distortion sense, as demonstrated by today's image and video coding standards, we show that superior transforms can be designed based on the complete transform coding model. We present a new class of transforms called coding-adaptive transforms, that are derived under an optimality criterion which incorporates the elements of transform coding and is formulated for coding efficiency. Additionally, characteristics of the new transforms such as adaptivity, generalization, and robustness are discussed.

The increased availability and usage of multimedia information have created a critical need for efficient multimedia processing algorithms. These algorithms must offer capabilities related to browsing, indexing, and retrieval of relevant data. A crucial step in multimedia processing is that of reliable video segmentation into visually coherent video shots through scene change detection. Video segmentation enables subsequent processing operations on video shots, such as video indexing, semantic representation, or tracking of selected video information. Since video sequences generally contain both abrupt and gradual scene changes, video segmentation algorithms must be able to detect a large variety of changes. While existing algorithms perform relatively well for detecting abrupt transitions (video cuts), reliable detection of gradual changes is much more difficult. A novel one-pass, real-time approach to video scene change detection based on statistical sequential analysis and operating on a compressed multimedia bitstream is proposed. Our approach models video sequences as stochastic processes, with scene changes being reflected by changes in the characteristics (parameters) of the process. Statistical sequential analysis is used to provide an unified framework for the detection of both abrupt and gradual scene changes.