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In order to protect the copyright of the color image, a novel robust color image watermarking method using correlations of RGB channels is presented. RGB three channels of the color image have much strong correlations, which are stable under various image attacks, and thus these correlations can be mined to embed watermark for robustness. In order to keep RGB correlations and chrominance perception, the color image is considered as the third-order tensor, and tucker decomposition is employed to operate on the color image. At first, Tucker decomposition is used to generate the first feature image, which includes the most of image energies and correlations between three channels. Then, the first feature image is divided into non-overlap blocks, and the singular value decomposition (SVD) is used to decompose the block to compute the left-singular matrix. Finally, the stable coefficients relationship of the left-singular matrix is modified to embed watermark for obtaining the robustness. Experimental results show that the proposed method outperforms other existing color image watermarking methods, which can resist JPEG compression, salt & pepper noise, median filtering, scaling, blurring, low-pass filtering, and so on attacks.

Recently, the perceived quality measurement of screen content images (SCIs) has become an active research topic. In this paper, a blind image quality measurement (IQM) metric for SCIs based on the learning of sparse features via dictionary learning is proposed. First, to extract the sparse features, histogram representations from multi-scale local gradient patterns are integrated to form a dictionary. Subsequently, using a pursuit algorithm, the sparse features of the distorted SCIs are efficiently coded by this dictionary. Finally, to obtain the final quality of the distorted SCIs, a machine learning algorithm is utilised to combine the sparse features into a final quality score. The results of extensive simulations conducted show that the proposed blind IQM metric consistently obtains competitive performance and is in line with human beings perceive.

Slight postharvest bruises of loquats remarkably affect the quality and shelf life of the fruits, but they are difficult to identify using visual inspection. Sub-surface structural changes in cells caused by mechanical injury or impact can be detected using spectroscopy-based methods from different angles. Optical coherence tomography (OCT), a non-destructive technology, can acquire cross-sectional images to analyze sub-surface structures of loquats, thus offering the potential to identify fruit bruises. This study proposes an automated OCT image processing method for extracting large cells from loquat images, which involves a series of steps including image denoising, boundary detection, filtering, binarization, segmentation, and region selection. Parenchyma cells in loquat tissue were visualized and characterized, and the five-cell morphological parameters, including total cell surface area, average cell surface area, average cell Feret diameter, equivalent diameter, and cell amount were measured. The bruised and non-bruised loquat groups showed significant differences in the total cell surface area and cell amount, suggesting that these two parameters might be used as indictors for bruise identification. No significant differences in other parameters were observed between the two groups. The microcosm approach proposed in this study sheds some light on ways to improve fruit quality evaluation. Overall, combined with appropriate image processing, OCT is an efficient and non-destructive tool for loquat bruise detection. The proposed strategy might also be expanded to other agricultural applications.

Scientific Reports 6: Article number: 33819; published online: 26 September 2016; updated: 23 February 2017 In the original version of this Article, all instances of “freshwater” were incorrectly given as “fleshwater”. This error has now been corrected in the PDF and HTML versions of the Article.

Most of Image Quality Assessment (IQA) metrics consist of two processes. In the first process, quality map of image is measured locally. In the second process, the last quality score is converted from the quality map by using the pooling strategy. The first process had been made effective and significant progresses, while the second process was always done in simple ways. In the second process of the pooling strategy, the optimal perceptual pooling weights should be determined and computed according to Human Visual System (HVS). Thus, a reliable spatial pooling mathematical model based on HVS is an important issue worthy of study. In this paper, a new Visual Perceptual Pooling Strategy (VPPS) for IQA is presented based on contrast sensitivity and luminance sensitivity of HVS. Experimental results with the LIVE database show that the visual perceptual weights, obtained by the proposed pooling strategy, can effectively and significantly improve the performances of the IQA metrics with Mean Structural SIMilarity (MSSIM) or Phase Quantization Code (PQC). It is confirmed that the proposed VPPS demonstrates promising results for improving the performances of existing IQA metrics.