In this paper, a novel wavelet-based composite mesh coding scheme is presented. In contrast to the state-of-the-art scalable interband mesh codec, the proposed codec relies on composite dependency models that capture ...
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ISBN:
(纸本)9781424456536
In this paper, a novel wavelet-based composite mesh coding scheme is presented. In contrast to the state-of-the-art scalable interband mesh codec, the proposed codec relies on composite dependency models that capture both the interband and intraband statistical dependencies between wavelet coefficients. Additionally, each wavelet subband is processed independently, which allows for parallelized processing and for a progressive reconstruction of each mesh resolution level. Compared to the state-of-the-art, the proposed codec yields for almost all rate points superior compression performance in L-2 -sense. Furthermore, the generated bitstreams are near-optimal in rate-distortion sense, which eliminates the need of a post-compression rate-distortion optimization technique.
In this paper, we propose a new coding scheme and establish new bounds on the capacity region for the multi-sender unicast index-coding problem. We revisit existing partitioned distributed composite coding (DCC) propo...
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In this paper, we propose a new coding scheme and establish new bounds on the capacity region for the multi-sender unicast index-coding problem. We revisit existing partitioned distributed composite coding (DCC) proposed by Sadeghi et al. and identify its limitations in the implementation of multi-sender composite coding and in the strategy of sender partitioning. We then propose two new coding components to overcome these limitations and develop a multi-sender cooperative composite coding (CCC). We show that CCC can strictly improve upon partitioned DCC, and is the key to achieve optimality for a number of index-coding instances. The usefulness of CCC and its special cases is illuminated via non-trivial examples, and the capacity region is established for each example. Comparisons between CCC and other non-cooperative schemes in recent works are also provided to further demonstrate the advantage of CCC.
The conventional single-pixel imaging (SPI) is unable to directly obtain the target's depth information due to the lack of depth modulation and corresponding decoding. The existing SPI-based depth imaging systems ...
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ISBN:
(数字)9781510630925
ISBN:
(纸本)9781510630925
The conventional single-pixel imaging (SPI) is unable to directly obtain the target's depth information due to the lack of depth modulation and corresponding decoding. The existing SPI-based depth imaging systems utilize multiple single-pixel detectors to capture multi-angle images, or introduce depth modulation devices such as optical grating to achieve three-dimensional imaging. The methods require bulky systems and high computational complexity. In this paper, we present a novel and efficient three-dimensional SPI method that does not require any additional hardware compared to the conventional SPI system. Specifically, a multiplexing illumination strategy combining random and sinusoidal pattern is proposed, which is able to simultaneously encode the target's spatial and depth information into a measurement sequence captured by a single-pixel detector. To decode the three-dimensional information from one-dimensional measurements, we built and trained a deep convolutional neural network. The end-to-end framework largely accelerates reconstruction speed, reduces computational complexity and improves reconstruction precision. Both simulations and experiments validate the method's effectiveness and efficiency for depth imaging.
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