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Wyszukujesz frazę "model selection optimization" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
HEVC Encoding Assisted with Noise Reduction
Autorzy:
Stankiewicz, O.
Wegner, K.
Karwowski, D.
Stankowski, J.
Klimaszewski, K.
Grajek, T.
Powiązania:
https://bibliotekanauki.pl/articles/226110.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
HEVC encoding
encoder control
model selection optimization
noise reduction
Opis:
Optimization of encoding process in video compression is an important research problem, especially in the case of modern, sophisticated compression technologies. In this paper, we consider HEVC, for which a novel method for selection of the encoding modes is proposed. By the encoding modes we mean e.g. coding block structure, prediction types and motion vectors. The proposed selection is done basing on noise-reduced version of the input sequence, while the information about the video itself, e.g. transform coefficients, is coded basing on the unaltered input. The proposed method involves encoding of two versions of the input sequence. Further, we show realization proving that the complexity is only negligibly higher than complexity of a single encoding. The proposal has been implemented in HEVC reference software from MPEG and tested experimentally. The results show that the proposal provides up to 1.5% bitrate reduction while preserving the same quality of a decoded video.
Źródło:
International Journal of Electronics and Telecommunications; 2018, 64, 3; 285-292
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of bayesian and other approaches to the estimation of fatigue crack growth rate from 2D textural features
Autorzy:
Mojzeš, M.
Kukal, J.
Lauschmann, H.
Powiązania:
https://bibliotekanauki.pl/articles/279255.pdf
Data publikacji:
2017
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
quantitative fractography
optimization
heuristic
linear regression
sub-model selection
Opis:
The fatigue crack growth rate can be explained using features of the surface of a structure. Among other methods, linear regression can be used to explain crack growth velocity. Nonlinear transformations of fracture surface texture features may be useful as explanatory variables. Nonetheless, the number of derived explanatory variables increases very quickly, and it is very important to select only few of the best performing ones and prevent overfitting at the same time. To perform selection of the explanatory variables, it is necessary to assess quality of the given sub-model. We use fractographic data to study performance of different information criteria and statistical tests as means of the sub-model quality measurement. Furthermore, to address overfitting, we provide recommendations based on a cross-validation analysis. Among other conclusions, we suggest the Bayesian Information Criterion, which favours sub-models fitting the data considerably well and does not lose the capability to generalize at the same time.
Źródło:
Journal of Theoretical and Applied Mechanics; 2017, 55, 4; 1269-1278
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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