Simple and effective image quality assessment (IQA) method is very desirable in many image and video processing applications, such as coding, transmission, restoration and enhancement. Classic pixel absolute error bas...
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ISBN:
(纸本)9781479973408
Simple and effective image quality assessment (IQA) method is very desirable in many image and video processing applications, such as coding, transmission, restoration and enhancement. Classic pixel absolute error based objective IQA metrics such as mean square error (MSE) and corresponding peak signal to noise ratio (PSNR) are widely used for various applications due to low computation and clear physical meanings, but have also been criticized for poorly correlated with subjective evaluation. Inspired by that human visual system (HVS) is more sensitive to image local edge distortion than flat or texture areas, in this paper, we propose a novel edge enhanced MSE (EE-MSE) to emphasize edge distortion effects on IQA. Experimental results on LIVE database release 2 show that the proposed EE-MSE IQA metric is competitive with state-of-the-art HVS-based IQA metrics, while has lower computational complexity and is more suitable for optimization task.
This paper presents a simple but effective macroblock (MB) layer rate control (RC) scheme for H.264/AVC with low complexity. First, to reduce computation cost and inaccuracy of linear mean absolute difference (MAD) pr...
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This paper presents a simple but effective macroblock (MB) layer rate control (RC) scheme for H.264/AVC with low complexity. First, to reduce computation cost and inaccuracy of linear mean absolute difference (MAD) prediction at MB layer adopted in JVT-G012, MAD is computed directly according to the difference between current original MB and the reference blocks pointed by estimated MV using intensive motion similarity. Then, MB header bits are predicted based on spatial-temporal correlation because it is not constant due to complicated coding modes and high compression efficiency of H.264. Finally, MB target bit rate is allocated according to its complexity and the parameters of quadratic R-D model are updated using coded MBs with high spatial-temporal correlation and motion similarity not the last coded data points. Simulation results show that the proposed scheme achieves an average PSNR gain of 0.37 dB, meets better with target bit rate, produces more consistent quality for the MBs in a frame and thus improves visual quality compared to classic JVT-H017 RC algorithm, simultaneously has lower computation complexity and suits for real-time application.
Recently, improving the perceptual quality of video coding under bit rate constraint has attracted researchers' attention in video coding field. In this paper, we propose to improve the coding perceptual quality f...
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Recently, improving the perceptual quality of video coding under bit rate constraint has attracted researchers' attention in video coding field. In this paper, we propose to improve the coding perceptual quality for the latest High Efficiency Video Coding (HEVC) standard using saliency guided coding tree unit (CTU) level quantization parameter (QP) adjustment scheme. Our work mainly includes two steps: first, a CTU layer saliency detection scheme with low complexity is proposed to measure the visual significance for each CTU in a frame. Then, a CTU layer QP adjustment scheme based on relative saliency is proposed to increase the bit rate resource for more salient regions and decrease the bit rate cost for less salient regions. Therefore, the proposed QP adjustment scheme obtains better bit rate allocation among CTUs and generates better coding perceptual quality. Experimental results show that the proposed method achieves better perceptual quality and rate distortion performance for HEVC video coding.
It is very important to find diversity measure when to perform a resampling step in particle filter. By analyzing the inherent deficiency in resampling algorithm of particle filter, some diversity measures including e...
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This paper proposes an improved image interpolation method based on the soft-decision adaptive interpolation (SAI) algorithm. Natural images often contain repeatable patterns and structures throughout the image, which...
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This paper proposes an improved image interpolation method based on the soft-decision adaptive interpolation (SAI) algorithm. Natural images often contain repeatable patterns and structures throughout the image, which is called non-local property. We can use this non-local strategy to improve the interpolation quality by better estimating the model parameters and Lagrangian multiplier. There are two steps in our method. In the first step, similar patches of the given block are found in the initialized high resolution image, and the model parameters can be determined properly using the expanded piecewise auto regression (PAR) model and non-local spatial constraint. In the second step, the self-similarity of patches across the high and low resolution images is exploited to solve the Lagrangian multiplier λ, thus to make the data estimation robust. Experiments indicate that the improved method can achieve good results both subjectively and objectively.
This paper introduces a new kind of recovery method which is the combination of Bayesian estimation and wavelet threshold. Wavelet coefficients of signals show strong characteristics of the non-Gauss statistics, its p...
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For learning-based super-resolution reconstruction, the selection and training of dictionary play an important role in improving image reconstruction quality. A super-resolution algorithm based on two dictionary-pairs...
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It is very important to find a criterion when to perform a resampling step. Aimed at this problem, an adaptive resampling algorithm in particle filter based on diversity measures is presented. Based on the analysis an...
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Face recognition (FR) systems are easily constrained by complex environmental situations in the wild. To ensure the accuracy of FR systems, face image quality assessment (FIQA) is applied to reject low-quality face im...
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Mutual information (MI) is an important information theoretic concept which has many applications in telecommunications, in blind source separation, and in machine learning. More recently, it has been also employed fo...
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