Vq-based Bayesian Estimation for Blur Identification and Image Selection in Video Sequences
نویسندگان
چکیده
We address the problem of blur identification and image selection with statistical blur priors in the context of the vector quantization (VQ) based framework. Firstly, we assume some dominant blur priors for estimating point spread functions (PSFs) of blurred frames in Bayesian MAP estimation. The blurred frames with estimated PSFs can be stored in VQ-based multiple codebooks. These codebooks can thus be used for identifying blurred video frames via VQ encoding distortion measure. Secondly, vector quantizer codebooks supply incorporate prior incrementally to the Bayesian learning process. The probabilistic model predicts and adds new codebooks dynamically for identifying more blurred frames in a video sequence. Experimental results demonstrate that the method offers an efficient way for practical blur identification and image selection in video sequences.
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