Joint histogram is the only quantity required to calculate the mutual information (MI) between two images. For MI based image registration, joint histograms are often estimated through linear interpolation or partial volume interpolation (PVI). It has been pointed out that both methods may result in a phenomenon known as interpolation induced artifacts. In this paper, we implemented a wide range of interpolation/approximation kernels for joint histogram estimation. Some kernels are nonnegative. In this case, these kernels are applied in two ways as the linear kernel is applied in linear interpolation and PVI. In addition, we implemented two other joint histogram estimation methods devised to overcome the interpolation artifact problem. They are nearest neighbor interpolation with jittered sampling with/without histogram blurring and data resampling. We used the clinical data obtained from Vanderbilt University for all of the experiments. The objective of this study is to perform a comprehensive comparison and evaluation of different joint histogram estimation methods for MI based image registration in terms of artifacts reduction and registration accuracy.
For video coding in futuristic ubiquitous environments, how to efficiently manage the power consumption while preserving high video quality is crucial. To address the above challenge elegantly, we formulate a multiple objective optimization problem to model the behavior of power-distortion-optimized video coding. Though the objectives in this problem are incommensurate and in conflict with one another. By assessing the performance trade-offs as well as the collective impact of power and distortion, we propose a joint power distortion control strategy (JPDC), in which the power and distortion are jointly considered. After the analysis on the approach of solving the problem statically, we utilize a sub-optimal “greedy” approach in the JPDC scheme. Each complexity parameter is adjusted individually. The system starts coding at the highest complexity level, and will automatically migrate to lower/higher level until the performance improvement gets saturated, leading to the optimal operation point. We perform simulations to demonstrate the effectiveness of the proposed scheme. Our results show that the proposed JPDC scheme is aware of the power constraint as well as the video content, and achieves significant power savings with well-perceived video quality. Such a feature is particularly desirable for futuristic video applications.
In video coding and streaming over wireless communication network,
the power-demanding video encoding operates on the mobile devices with limited energy supply. To analyze, control, and optimize the rate-distortion (R-D) behavior of the wireless video communication
system under the energy constraint, we need to develop a power-rate-distortion (P-R-D) analysis framework, which extends the traditional R-D analysis by including another dimension, the power consumption.
Specifically, in this paper, we analyze the encoding mechanism of typical video encoding systems and develop a parametric video encoding architecture which is fully scalable in computational complexity. Using dynamic voltage scaling (DVS), a hardware technology recently developed in CMOS circuits design, the complexity scalability can be translated into the power consumption scalability of the video encoder. We investigate the rate-distortion behaviors of the complexity control parameters and establish an analytic framework to explore the P-R-D behavior of the video encoding system. Both theoretically and experimentally, we show that, using this P-R-D model, the encoding system is able to automatically adjust its complexity control parameters to match the available energy supply
of the mobile device while maximizing the picture quality. The P-R-D model provides a theoretical guideline for system design and performance optimization in wireless video communication under energy constraint, especially over the wireless video sensor network.