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Your search keyword '"da Silva Cruz, Luis A."' showing total 2 results
Start Over Author "da Silva Cruz, Luis A." Topic image compression
2 results on '"da Silva Cruz, Luis A."'

1. Lossless coding of light field images based on minimum-rate predictors.

Author

Santos, João M., Assuncao, Pedro A.A., da Silva Cruz, Luis A., Tavora, Luis M.N., Fonseca-Pinto, Rui, and Faria, Sergio M.M.

Subjects
*LIGHT-field cameras, *IMAGE processing, *IMAGE quality analysis, *COMPUTATIONAL photography, *IMAGE compression
Abstract

Recent developments in light field acquisition and computational photography are driving new research efforts on light field encoding methods, capable of exploiting the specific features of this type of visual data. This paper presents a research study of lossless light field image compression, using Minimum-Rate Predictors (MRP) and mainstream image and video encoders. The research is focused on three light field representation formats: lenslet images, stack of sub-aperture images and epipolar images. The main contributions of this work are the ‘Spiral-blackend’ serialization method and the use of MRP for the lossless compression of light fields with joint encoding of RGB data. The results show that the lenslet format yields lower compression efficiencies than other formats. Furthermore, it is demonstrated that the MRP algorithm consistently outperforms HEVC-RExt, JPEG2000, JPEG-LS and CALIC when light fields are represented by either a stack of sub-aperture or epipolar images. [ABSTRACT FROM AUTHOR]

Published
2018
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2. A Method for Automatic Detection of Rectangular Regions of Interest in Arbitrary Images.

Author

Martins-Ferreira, Nelson, Ferreira, Lino, Pascoal-Faria, Paula, da Silva Cruz, Luis A., Assuncao, Pedro, and Alves, Nuno

Subjects
*RECTANGULAR plates (Engineering), *COMPUTATIONAL complexity, *PIXELS, *IMAGE analysis, *IMAGE compression
Abstract

This paper presents a computational method to extract optimum rectangular Regions of Interest (RoI) in images with an associated saliency map. Although saliency maps provide an individual relevance measure for each pixel, to find the sub-image (i.e., rectangular region) that contains the set of the most relevant pixels requires an optimisation procedure to define the boundaries of the best RoI. This is achieved by the method devised in the paper, by following an approach based on balancing the amount of relevant information that is included and excluded from the RoI. The results show that such method is capable of finding the most relevant rectangular RoI and thus to extract the optimum sub-images according to the relevance measure given by a generic saliency map. Since the method is not tied to any particular type of images, it finds application in quite different fields, such as salient object extraction and processing in industry and surveillance, image compression using attention modelling, biomedical imaging, etc. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
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