Fast block matching using prediction and rejection criteria

Liaw, Yi‐Ching; Lai, Jim Z. C.; Hong, Zuu‐Chang

doi:10.1016/j.sigpro.2008.12.012

Cited by 20 publications

(3 citation statements)

References 15 publications

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“…In BMAs, successive sky images are partitioned into macro blocks of pixels. Then, for each block in the current image, BMAs seek the best matching block in the previous image, according to some matching criteria ( Liaw et al, 2009 ). The Exhaustive Search (ES) block matching algorithm provides the best performance by matching all possible blocks within the search area.…”

Section: Resultsmentioning

confidence: 99%

Towards the intrahour forecasting of direct normal irradiance using sky-imaging data

Nou

Chauvin

Eynard

et al. 2018

Heliyon

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Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is directly impacted by both the availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). The present paper deals with a new approach to the intrahour forecasting (the forecast horizon normalΔtf is up to 300.25emmin ahead) of DNI, taking advantage of the fact that this quantity can be split into two terms, i.e. clear-sky DNI and the clear sky index. Clear-sky DNI is forecasted from DNI measurements, using an empirical model (Ineichen and Perez, 2002) combined with a persistence of atmospheric turbidity. Moreover, in the framework of the CSPIMP (Concentrating Solar Power plant efficiency IMProvement) research project, PROMES-CNRS has developed a sky imager able to provide High Dynamic Range (HDR) images. So, regarding the clear-sky index, it is forecasted from sky-imaging data, using an Adaptive Network-based Fuzzy Inference System (ANFIS). A hybrid algorithm that takes inspiration from the classification algorithm proposed by Ghonima et al. (2012) when clear-sky anisotropy is known and from the hybrid thresholding algorithm proposed by Li et al. (2011) in the opposite case has been developed to the detection of clouds. Performance is evaluated via a comparative study in which persistence models – either a persistence of DNI or a persistence of the clear-sky index – are included. Preliminary results highlight that the proposed approach has the potential to outperform these models (both persistence models achieve similar performance) in terms of forecasting accuracy: over the test data used, RMSE (the Root Mean Square Error) is reduced of about 200.25emW0.2emm−2, with normalΔtf=150.25emmin, and 400.25emW0.2emm−2, with normalΔtf=300.25emmin.

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Section: Resultsmentioning

confidence: 99%

Towards the intrahour forecasting of direct normal irradiance using sky-imaging data

Nou

Chauvin

Eynard

et al. 2018

Heliyon

View full text Add to dashboard Cite

show abstract

“…And then, for each block at the current image, the algorithm seeks for the best matching block within a search area from the previous image according to a matching criterion. The exhaustive search ES provides the best performance by matching all possible blocks within the search area, but the search process takes a significant computation time [5].…”

Section: Introductionmentioning

confidence: 99%

Clouds Motion Estimation from Ground-Based Sky Camera and Satellite Images

Zaher¹,

Ghanem²

2018

Colorimetry and Image Processing

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Estimation of cloud motion is a challenging task due to the non-linear phenomena of cloud formation and deformation. Satellite images processing is a popular tool used to study the characteristics of clouds which constitute major factors in forecasting the meteorological parameters. Due to the low resolution of satellite images, researchers have turned towards analyzing the high-resolution images captured by ground-based sky cameras. The first objective of this chapter is to demonstrate the different techniques used to estimate clouds motion and to compare them with respect to the accuracy and the computational time. The second aim is to propose a fast and efficient block matching technique based on combining the two types of images. The first idea of our approach is to analyze the low-resolution satellite images to detect the direction of motion. Then, the direction is used to orient the search process to estimate the optimal motion vectors from the high-resolution ground-based sky images. The second idea of our method is to use the entropy technique to find the optimal block sizes. The third idea is to imply an adaptive cost function to perform the matching process. The comparative study demonstrates the high performance of the proposed method with regards to the robustness, the accuracy and the computation time.

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“…Figure 1 show some of the search patterns taken by UMHEXS algorithm at different stages in the algorithm. Researchers are developing the algorithms for content aware ME to make the search more adaptive (Yi-Ching Liawa and Zuu-ChangHong, 2009). Characteristics of video data can be in terms of object acceleration, inertia, linearity (DongYoon Kim and Park, 2013) of the object etc.…”

Section: Introductionmentioning

confidence: 99%

Random Initial Search Points Prediction for Content Aware Motion Estimation in H.264

More¹,

Deshmukh²,

More³

et al. 2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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Motion estimation algorithms used in video encoders are based on three important issues: selection of good initial search points, choice of appropriate search pattern and effective early termination criteria at different stages in algorithm.Motion vector prediction is also treated as initial search point prediction, in which possibility of good match block is predicted. Prediction is based on prior data from co-located and/or adjacent macroblocks from reference frame or current video frame respectively.Different search patterns contribute in achieving near accurate motion estimation. Different types of motion in real time videos can be tracked using different types of patterns. Early termination criteria at different stages in algorithm, avoid search at further possible locations which are pre-decided by pattern of search. This in turns reduces computations and motion estimation time.Proposed algorithm is combination of two concepts, content awareness and initial point prediction.Contents of video data is in terms of homogeneity coefficients. Initial search point prediction is used to avoid the search trapping into local minima.The algorithm is implemented on Reference Software of JM18.4 of H.264/AVC revised on 5th May 2011. The results of the implemented algorithm show that the total time taken for encoding and motion estimation time are less as compared with other algorithms for the videos of different resolutions.

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Fast block matching using prediction and rejection criteria

Cited by 20 publications

References 15 publications

Towards the intrahour forecasting of direct normal irradiance using sky-imaging data

Towards the intrahour forecasting of direct normal irradiance using sky-imaging data

Clouds Motion Estimation from Ground-Based Sky Camera and Satellite Images

Random Initial Search Points Prediction for Content Aware Motion Estimation in H.264

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