1,720,968 research outputs found
Robust video communication for peer-to-peer streaming using slice reordering and error protection codes
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Concealment driven smart slice reordering for robust video transmission
No full textIn this paper we address a novel scheme to protect video sequences according to slice importance based on slice reordering, ULP and error-concealment techniques. The approach does not require the modification to the video decoder although an application-layer channel coding is required. Simulation results show that the proposed algorithm outperforms state-of-the-art approaches, reducing the gap with the upper-bound error-free performance curve. Moreover, the complexity of the additional stage required to pilot the protection allocation stage is negligible with respect to traditional ULP schemes
On Modeling Mismatch Errors Induced by Different Quantizers
No full textIn this letter, the mismatch error due to the replacement of a fine with a coarse quantizer is considered, and an analytical model is proposed to describe the related distortion. Simulations show that this model is highly accurate and can be used to estimate the expected distortion of DPCM-based codecs in order to better allocating the rate. For highly correlated sources, this leads to a gain of 1 to 1.5 dB over an exhaustive search method that adopts a uniform redundancy allocation. Moreover, it permits to allocate the redundancy by an easy-to-solve analytical model of the system
An End-to-End Framework for the Classification of Hyperspectral Images in the Wood Domain
Full text linkHyperspectral images consist of a multitude of spectral bands for each pixel. Spectral bands provide information about wavelengths that may cover a larger spectrum of what the human eye may see. In the hyperspectral domain, the classification of hyperspectral images is usually addressed by taking into account only the spectral information. However, in the wood domain, spatial information is also relevant. To bridge this gap, this paper proposes a CNN-based end-to-end framework for the classification of hyperspectral images in the wood domain. The proposed framework consists of a spatial and spectral classifier that are integrated to make the final prediction. Each classifier is built by adapting a general image classifier, which is suitable for the classification of three-band images, to handle hyperspectral images. The framework is trained and validated on a real dataset, provided by a company working in the wood domain to detect wood fungi. The results obtained have shown that the proposed framework is a lightweight and effective approach for the recognition of wood fungi categories. The framework outperforms a benchmark classifier by 17% and can generate a classification map of hyperspectral images of wood boards of any size with an accuracy of 96%
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