We calculate photometric redshifts from the Sloan Digital Sky Survey Data Release 2 (SDSS DR2) Galaxy Sample using artificial neural networks (ANNs). Different input sets based on various parameters (e.g. magnitude, color index, flux information) are explored. Mainly, parameters from broadband photometry are utilized and their performances in redshift prediction are compared. While any parameter may be easily incorporated in the input, our results indicate that using the dereddened magnitudes often produces more accurate photometric redshifts than using the Petrosian magnitudes or model magnitudes as input, but the model magnitudes are superior to the Petrosian magnitudes. Also, better performance results when more effective parameters are used in the training set. The method is tested on a sample of 79 346 galaxies from the SDSS DR2. When using 19 parameters based on the dereddened magnitudes, the rms error in redshift estimation is σ z = 0.020184. The ANN is highly competitive tool compared to the traditional template-fitting methods when a large and representative training set is available.
To decrease the computational complexity of adaptive inter-layer prediction and improve the encoding efficiency in spatial and temporal scalable video coding, a mode decision algorithm is proposed by exploiting the part of used modes in the co-located reference macroblock(s) for Hierarchical-B pictures of all layers. This scheme generates a reduced dynamic candidate list for the current macroblock according to the statistical information derived from the co-located reference macroblocks in their present temporal level. The experimental results show that this algorithm compresses approximately 29% in encoding time with the negligible loss in PSNR and slightly increasing in bit rate
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