Fundamentals of Electronic Imaging Systems

Schreiber, William F.

doi:10.1007/978-3-642-77847-6

Cited by 83 publications

(23 citation statements)

References 0 publications

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“…The human contrast perception in complex images varies with the local image content [12]. According to Weber-Fechner Law [13], HVS responds more to the local variations of the surrounding background than the absolute luminance. Contrast is the measure of this relative variation.…”

Section: Typical Hvs Modelmentioning

confidence: 99%

Color image denoising in wavelet domain using adaptive thresholding incorporating the Human Visual System model

Laskar

Baishya

Kar

et al. 2010

International Conference on Electrical &Amp; Computer Engineering (ICECE 2010)

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This paper utilizes the characteristics of the Human Visual System (HVS) for obtaining better results in denoising color images corrupted by Gaussian noise. Implementation of the Contrast Sensitivity Function (CSF) of the HVS is done in each of the sub-bands in the wavelet domain. In this paper, an 11-weight Invariant Single Factor (ISF) system is proposed and implemented which outperforms the generally used 6-weight ISF system. The major strength of the incorporation of this vision model is the optimum way in which the scales are distributed among the luminance and chrominance components to achieve better image quality without color degradation. Two adaptive thresholding schemes in the wavelet domain have been used in the denoising process. To evaluate the performance of the algorithm universal image quality index (Q) and Peak Signal-to-Noise Ratio (PSNR) have been used.

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Section: Typical Hvs Modelmentioning

confidence: 99%

Color image denoising in wavelet domain using adaptive thresholding incorporating the Human Visual System model

Laskar

Baishya

Kar

et al. 2010

International Conference on Electrical &Amp; Computer Engineering (ICECE 2010)

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“…Luminance signals generally contain more than 60% of the total energy of the original signal, with the chrominance signals each having less than 20%. HVS response depends much less on the absolute luminance than on the relation to its local variations to the surrounding background, a property known as Weber-Fechnew law [16]. Furthermore, it has been found that strong chrominance edges are accompanied by strong luminance edges, but the reverse of this is not true, i.e., there are many luminance edges that are not accompanied by chrominance edges [14].…”

Section: Adaptive Weights Using Chrominance Informationmentioning

confidence: 99%

Improved Update Step for Scalable Video Coding in Video Surveillance

Ling

Yang

2007

J VLSI Sign Process Syst Sign Im

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Many evolving video services and applications for intelligent security systems require reliable transmission of high quality video to diverse clients over heterogeneous networks using available system resources. Scalable video coding (SVC) is one of the emerging video compression technologies with such potential capabilities. Advances in lifting-based motion-compensated temporal filtering (MCTF) have enabled highly efficient and flexible spatial, temporal, signal-to-noise ratio (SNR), and complexity scalability to be realized over a wide range of bit rates. In this paper, we present an algorithm to improve the update step of MCTF, which serves as an important informative step for the coding performance of SVC. A novel update-step algorithm, which takes advantage of the chrominance information of the video sequence and the correlation of the motion vectors (MVs) of the neighboring blocks as well as the correlation of the derived update MVs in the low-pass frames, is proposed to improve update step of MCTF by (1) computing correct update motion information, (2) generating correct amount of energy contained in the high-pass frames. Experimental results show that the proposed algorithm can significantly improve the quality of the reconstructed video sequence in visual quality.

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“…The response of the HVS depends much less on the absolute luminance than on the relation of its local variations to the surrounding background, a property known as Weber-Fechner law [25]. Contrast is a measure of this relative variation, which is commonly used in vision models.…”

Section: Contrast and Adaptationmentioning

confidence: 99%