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Uniform continuity bounds for information characteristics of quantum channels depending on input dimension and on input energy
摘要： We obtain continuity bounds for basic information characteristics of quantum channels depending on their input dimension (if it is finite) and on the input energy bound (if the input dimension is infinite). We pay special attention to the case in which a multimode quantum oscillator is an input system. First, we prove continuity bounds for the output conditional mutual information for a single channel and for n copies of a channel. Then we obtain estimates for the variation of the output Holevo quantity with respect to simultaneous variations of a channel and of an input ensemble. As a result, tight and close-to-tight continuity bounds for basic capacities of quantum channels, which depend on the input dimension, are obtained. They complement the Leung–Smith continuity bounds, which depend on the output dimension. Finally, we obtain tight and close-to-tight continuity bounds for basic capacities of infinite-dimensional energy-constrained channels with respect to the energy-constrained Bures distance generating the strong convergence of quantum channels.
关键词： strong convergence of quantum channels,ensemble of quantum states,the Holevo quantity,quantum conditional mutual information,quantum channel capacities,energy-constrained Bures distance,multi-mode quantum oscillator
更新于2025-09-04 15:30:14
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[Lecture Notes in Computer Science] Algorithms and Architectures for Parallel Processing Volume 11335 (18th International Conference, ICA3PP 2018, Guangzhou, China, November 15-17, 2018, Proceedings, Part II) || SMIM: Superpixel Mutual Information Measurement for Image Quality Assessment
摘要： The image quality assessment (IQA) is a fundamental problem in signal processing that aims to measure the objective quality of an image by designing a mathematical model. Most full-reference (FR) IQA methods use ?xed sliding windows to obtain structure information but ignore the variable spatial con?guration information. In this paper, we propose a novel full-reference IQA method, named “superpixel normalized mutual information (SMIM)” based on the perspective of variable receptive ?eld and information entropy. First, we ?nd that consistence relationship exists between the information ?delity and human visual of individuals. Thus, we reproduce the human visual system (HVS) to semantically divide the image into multiple patches via superpixel segmentation. Then the weights of each image patches are adaptively calculated via its information volume. We veri?ed the e?ectiveness of SMIM by applying it to data from the TID2008 database and data generated using some real application scenarios. Experiments show that SMIM outperforms some state-of-the-art FR IQA algorithms, including visual information ?delity (VIF).
关键词： Superpixel segmentation,Mutual information,Image quality assessment
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Automatic Coregistration of SAR and Optical Images Exploiting Complementary Geometry and Mutual Information
摘要： Image coregistration aims at stacking two or multiple images in a way such that, for each image, the same pixel corresponds to the same point of the target scene (possibly with sub-pixel accuracy). We can distinguish two families of image coregistration problems, basically depending on if the images to be coregistered are taken by sensors of the same or different type (e.g., sensing different wavelenghts), and with similar or different illumination and acquisition geometries (e.g. different sun illumination conditions and/or different acquisition incidence angles).Whilst the first type of image coregistration is well established, multimodal coregistration is not yet well founded and due to difficulty of finding correspondences between the images (tie points) in a robust way, and the available approaches often recur to manual assistance. The multimodal image coregistration technique proposed in this work overcomes the problems due to differences in radiometries and in geometries by exploiting two main concepts: complementary geometry information between the images to be coregistered, and mutual information (or entropy) as similarity metric. The method focuses on coregistration of very high resolution synthetic aperture radar (SAR) and optical images, but the approach is of general validity. The tests performed on real very high resolution optical and SAR data confirm the validity of the method.
关键词： SAR feature extraction,image coregistration,mutual information,bundle adjustment,multimodal image coregistration
更新于2025-09-04 15:30:14