3D-LUTs are widely used in cinematography to map one gamut into another or to provide different moods to the images via artistic color transformations. Most of the time, these transformations are computed off-line and their sparse representations stored as 3D-LUTs into digital cameras or on-set devices. In this way, the director and the on-set crew can see a preview of the final results of the color processing while shooting. Unfortunately, these kind of devices have strong hardware constraints, so the 3D-LUTs shall be as small as possible, but always generating artefact-free images. While for the SDR viewing devices this condition is guaranteed by the dimension 33×33×33, for the new HDR and WCG displays much larger and not feasible 3DLUTs are needed to generate acceptable images. In this work, the uniform lattice constrain of the 3D-LUT has been removed. Therefore, the position of the vertices can be optimized by minimizing the color error introduced by the sparse representation. The proposed approach has shown to be very effective in reducing the color error for a given 3D-LUT size, or the size for a given error.

Accurate facial skin colour representation is highly required for an increasing number of applications, such as the solution of cosmetic products, the diagnosis of cutaneous diseases, and the manufacture of soft tissue prostheses. This study presents a novel camera colour characterisation model with higher predictive accuracy for the image-based colour measurement of human skin. More specifically, a digital imaging system was developed to collect the facial images of sixty human subjects from four ethnic groups. The newly collected human facial skin colour data and a conventional colour chart were selected as the training dataset, respectively, and three general techniques (linear transformation, polynomial regression, and root-polynomial regression) were utilised to derive the characterisation model by mapping camera digital signals to CIE XYZ tristimulus values. The predictive accuracy of each model was then verified using the mean CIELAB colour difference between actual skin colour measurements and the corresponding predictions from colour images. Results showed that the best model performance was achieved when the human skin colours of real subjects were used as the training samples and first order polynomial regression was used as the mapping algorithm.

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts. Judging the quality for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder such as an OCR engine. We mainly study the quality of scanned documents in terms of the detection accuracy of its OCR-transcribed version. For this purpose, we proposed a novel CNN based model to predict the quality level of scanned documents or regions in scanned documents. Experimental results evaluated on our testing dataset demonstrate the effectiveness and efficiency of our method both qualitatively and quantitatively.

Modern scan routines require a predefined scan resolution, whether it is customer-selected or a default value in the scanner’s settings. When the scanning process begins, the resolution cannot be changed. This results in all scanned pages, no matter how much their contents may vary, having output images of the same size. If we can determine an optimal resolution for each scanned document raster content, we can save a lot of storage. In this paper, the resolutions in question are 300 dpi, 150 dpi, and 75 dpi. We define the criteria for optimal scan resolution and propose some new features to help determine it for scanned document raster content. The features proposed are sample power spectrum mean squared error (MSE), edge density, and edge contrast. These features can reflect the truthfulness between high-resolution 300 dpi images (references) and their lowresolution (150 dpi and 75 dpi) counterparts and the intrinsic changes among them. Combining them with spatial activity, tile standard deviation (STDDEV) structural similarity index measure mean (tile-STDDEV SSIM), and tile STDDEV structural similarity index measure STDDEV (tile-STDDEV SSIM STDDEV), we can form a feature vector, which is then fed into an SVM classifier. Test result shows that we can achieve a prediction accuracy of 93.4%.

Banding has been regarded as one of the most severe defects affecting the overall image quality in the printing industry. There has been a lot of research on it, but most of them focused on uniform pages or specific test images. Aiming at detecting banding on customer’s content pages, this paper proposes a banding processing pipeline that can automatically detect banding, identify periodic and isolated banding, and estimate the periodic interval. In addition, based on the detected banding characteristics, the pipeline predicts the overall quality of printed customer’s content pages and obtains predictions similar to human perceptual assessment.

In previous work [1] , content-color-dependent screening (CCDS) determines the best screen assignments for either regular or irregular haltones to each image segment, which minimizes the perceived error compared to the continuous-tone digital image. The model first detects smooth areas of the image and applies a spatiochromatic HVS-based model for the superposition of the four halftones to find the best screen assignment for these smooth areas. The segmentation is not limited to separating foreground and background. Any significant color regions need to be segmented. Hence, the segmentation method becomes crucial. In this paper, we propose a general segmentation method with a few improvements: The number of K-means clusters is determined by the elbow method to avoid assigning the number of clusters manually for each image. The noise removing bilateral filter is adaptive to each image, so the parameters do not need to be tested and adjusted based on the visual output results. Also, some color regions can be clearly separated out from other color regions by applying a color-aware Sobel edge detector.

The text fading defect is one of the most common defects in electrophotographic printers; and it dramatically affects print quality. It usually appears in a significant symbol Region of Interest (ROI), easily noticed by a user on his or her print. We can detect text fading by the density reduction for the black and white printed symbol ROI. It is difficult to detect the color text fading only by density reduction, because the depleted cartridge may only cause the color distortion without density reduction in the color printed symbol ROI. In our previous work with print quality defects analysis, the text fading detection method only works for black text fading defect detection [1]. Our new text fading method can detect the color text fading defect and predict the depleted cartridge. In this new text fading detection method, we use whole page image registration and the median threshold bitmap (MTB) matching method to align the text characters between the master and test symbol ROIs, because with the aligned text characters, it is easy to extract the difference between the master and the test text characters to detect the text fading defect. We use a support vector machine classifier to assign a rank to the overall quality of the printed page. We also use the gap statistic method with the K-means clustering algorithm to extract the different text characters’ different colors to predict the depleted cartridge.

Streaks are one of the most common print defects in electrophotographic printers that influence print quality. Streak defects are dark or light lines with the major axis along the printing process direction and are usually caused by a defective Intermediate Transfer Belt (ITB) or Organic Photo Conductor (OPC) component in the printer. Previously, we designed an algorithm to detect the streak defects only on standard printed test pages, which have uniform color [1]. In this paper, we design an algorithm to detect the streak defects on the customer content area, which we call a raster ROI. It is more complicated than the uniform color printed page because the customer content influences our streak detection result. Sometimes, the customer content has some dark or light straight lines along the printing process direction, and they are similar to streak defects. To detect the streak defects on the customer pages, we must separate the straight lines of customer content and the streak defects. To detect the streak in a raster ROI, we apply the Sobel edge detection algorithm and morphological operations to the master image, which includes the customer content without defects, to remove the straight customer content lines on the scanned test pages. The remaining dark or light straight line along the printing process direction may be streak defects. For the detected streak defects, we use the DAG-SVM multi-classification method to classify the rank of streak defects in the raster ROI.

In order to investigate the effect of ipRGC on color discrimination, it is necessary to reproduce two metameric light stimuli (we call these stimuli as metameric ipRGC stimuli) that have the same amount of cones and rods but different stimulus amount of ipRGC. However, it is difficult to independently control the amount of only ipRGC stimulation because the spectral sensitivity functions of the cones and rod overlap that of ipRGC in the wavelength band. So far, researchers have not addressed a comprehensive analysis of metameric ipRGC stimuli and color perception experiments for those stimuli. In this study, first, we proposed the calculation method of metameric ipRGC stimulus based on the orthogonal basis functions of human photoreceptors. Then, we clarified the controllable range of metameric ipRGC stimulus in the color gamut. Second, we conducted subjective evaluation experiments for investigating the discriminative colors due to metameric ipRGC stimuli. We showed the effects of ipRGC on color discrimination.