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Recently, parametric models of predicting video subjective quality by exploiting packet layer or bit stream layer information at decoder side has achieved significant progress. In contrast, there are few works on estimating subjective quality in the stage of video encoding, partially because of limited and outdated dataset. In this paper, we contribute a new 4K video dataset with full subjective scores...
In order to improve forecasting model accuracy of BP neural network, an improved prediction method of optimized BP neural network based on modified particle swarm optimization algorithm (PSO) was proposed. In this modified PSO algorithm, an adaptive mutation operator was proposed in PSO to change positions of the particles plunged in the local optimization. The modified PSO was used to optimize the...
Integrating efficient visual perceptual cues into standardized video coding framework can improve performance significantly. In this paper we propose to enhance AVS encoder by using the latest just noticeable distortion (JND) model to adjust DCT coefficients of prediction residues in a content adaptive way. To better modeling JND profile in AVS integer DCT domain, we further derive the JND mapping...
Foreground detection is an important task in computer vision applications. In this paper, we present an efficient foreground detection method based on a robust linear regression model. First, a novel framework is proposed where foreground detection has been cast as an outlier signal estimation problem in a linear regression model. We regularize this problem by imposing a so-called fused sparsity constraint,...
Sparse signal representation based on redundant dictionaries contributed to much progress in image processing in the past decades. But the common overcomplete dictionary model is not well structured and there is still no guideline for selecting the proper dictionary size. In this paper, we propose a new algorithm for dictionary learning based on subspace segmentation. Our algorithm divides the training...
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