In this paper, we compare the influence of a higherresolution Head-Mounted Display (HMD) like HTC Vive Pro on 360° video QoE to that obtained with a lower-resolution HMD like HTC Vive. Furthermore, we evaluate the difference in perceived quality for entertainment-type 360° content in 4K/6K/8K resolutions at typical high-quality bitrates. In addition, we evaluate which video parts people are focusing on while watching omnidirectional videos. To this aim we conducted three subjective tests. We used HTC Vive in the first and HTC Vive Pro in the other two tests. The results from our tests are showing that the higher resolution of the Vive Pro seems to enable people to more easily judge the quality, shown by a minor deviation between the resulting quality ratings. Furthermore, we found no significant difference between the quality scores for the highest bitrate for 6K and 8K resolution. We also compared the viewing behavior for the same content viewed for the first time with the behavior when the same content is viewed again multiple times. The different representations of the contents were explored similarly, probably due to the fact that participants are finding and comparing specific parts of the 360° video suitable for rating the quality.

Streaks are one of the most common defects in electrophotographic printers, and dramatically affect print quality. Researchers have developed methods to detect streaks. Then, using the detection result helps us to diagnose issues of the printer and discover broken components of the electrophotographic printer. In previous work, the streak detection methods are based on a particular printer or particular streak defects, such as Intermediate Transfer Belt (ITB) or Organic Photoconductor (OPC) drum streak. In this paper, we design a Block Window Method to pre-test the images with streak defects. It is based on the local ΔE value in a block window and works for different kinds of streaks. After using the Block Window Method, the detection result includes small streaks or noise defects that are too localized for humans to see. We use the logistic regression algorithm to classify the real visible streaks and small invisible streaks. This process will improve the accuracy of the detection result. After the classification, we can get the streak detection result, which is significant for extracting the feature vector of the streak defects in the test image. Then, we can use the feature vector to classify different streak defects.

Local defects are very common on printed pages. Automatic detection of such defects will help the product support personnel to diagnose the problem and fix it more efficiently. Among previous works on local defect detection on printed pages, most of them divide the printed page into small blocks and calculate the variation within each block. This method is time consuming and not robust in dealing with defects at different scales. In this paper, we propose a robust framework for detecting the local defects on scanned printed pages. To achieve the efficiency and robustness, our framework applies the Gaussian pyramids method and the selective search method. We also create manual features for classification to increase the detection accuracy. Finally, applying our method on printed pages demonstrates its efficacy.

The repetitive interval is a very crucial feature of bands in print quality assessment, because any irregularity on the surface of a rotating component localized in the circumference will incur repetitive defects on the output of printer [1] [2] [3]. Hence, the repetitive interval can help us diagnose the issues. In previous work, a cost function method provides a robust algorithm to predict the repetitive interval on less noisy samples. However, if the samples contain more aperiodic bands and noise, the estimation will become a challenge. Moreover, the missing periodic bands will decrease the probability of correct prediction. In this paper, we proposes a novel cost-function-based repetitive interval estimation method for periodic bands. By adding synthetic missing bands, we re-evaluate the cost function values to check whether it has a better result. We also show the improvement of accuracy on the print samples with our proposed algorithm.<xref ref-type="corresp" rid="cor1">1</xref>

Print quality is an important criterion for a printer’s performance. The detection, classification, and assessment of printing defects can reflect the printer’s working status and help to locate mechanical problems inside. To handle all these questions, an efficient algorithm is needed to replace the traditionally visual checking method. In this paper, we focus on pages with local defects including gray spots and solid spots. We propose a coarse-to-fine method to detect local defects in a block-wise manner, and aggregate the blockwise attributes to generate the feature vector of the whole test page for a further ranking task. In the detection part, we first select candidate regions by thresholding a single feature. Then more detailed features of candidate blocks are calculated and sent to a decision tree that is previously trained on our training dataset. The final result is given by the decision tree model to control the false alarm rate while maintaining the required miss rate. Our algorithm is proved to be effective in detecting and classifying local defects compared with previous methods.

In the last decades, many researchers have developed algorithms that estimate the quality of a visual content (videos or images). Among them, one recent trend is the use of texture descriptors. In this paper, we investigate the suitability of using Binarized Statistical Image Features (BSIF), the Local Configuration Pattern (LCP), the Complete Local Binary Pattern (CLBP), and the Local Phase Quantization (LPQ) descriptors to design a referenceless image quality assessment (RIQA) method. These descriptors have been successfully used in computer vision applications, but their use in image quality assessment has not yet been thoroughly investigated. With this goal, we use a framework that extracts the statistics of these descriptors and maps them into quality scores using a regression approach. Results show that many of the descriptors achieve a good accuracy performance, outperforming other state-of-the-art RIQA methods. The framework is simple and reliable.

Objective measurements of imaging system sharpness (Modulation Transfer Function; MTF) are typically derived from test chart images. It is generally assumed that if testing recommendations are followed, test chart sharpness (which we also call “chart quality”) will have little impact on overall measurements. Standards such as ISO 12233 [1] ignore test chart sharpness. Situations where this assumption is not valid are becoming increasingly frequent, in part because extremely high-resolution cameras (over 30 megapixels) are becoming more common and in part because manufacturing test stations, which have limited space, often use charts that are smaller than optimum. Inconsistent MTF measurements caused by limited chart sharpness can be problematic in manufacturing supply chains that require consistency in measurements taken at different locations. We describe how to measure test chart sharpness, fit the measurement to a model, quantify the effects of chart sharpness on camera system MTF measurements, then compensate for these effects using deconvolution–by dividing measured system MTF by a model of the chart MTF projected on the image sensor. We use results of measurements with and without MTF compensation to develop a set of empirical guidelines to determine when chart quality is • good enough so that no compensation is needed, and • too low to be reliably compensated.

We improve High Dynamic Range (HDR) Image Quality Assessment (IQA) using a full reference approach that combines results from various quality metrics (HDR-CQM). We combine metrics designed for different applications such as HDR, SDR and color difference measures in a single unifying framework using simple linear regression techniques and other non-linear machine learning (ML) based approaches. We find that using a non-linear combination of scores from different quality metrics using support vector machine is better at prediction than the other techniques such as random forest, random trees, multilayer perceptron or a radial basis function network. To improve performance and reduce complexity of the proposed approach, we use the Sequential Floating Selection technique to select a subset of metrics from a list of quality metrics. We evaluate the performance on two publicly available calibrated databases with different types of distortion and demonstrate improved performance using HDR-CQM as compared to several existing IQA metrics. We also show the generality and robustness of our approach using cross-database evaluation.

Viewers of high dynamic range television (HDR, HDR-TV) expect a comfortable viewing experience with significantly brighter highlights and improved details of darker areas on a brighter display. However, extremely bright images on a HDR display are potentially undesirable and lead to an uncomfortable viewing experience. To avoid the issues, we require specific production guidelines for subjective brightness to ensure brightness consistency between and within programs. To create such production guidelines, it is necessary to develop an objective metric for subjective brightness in HDR-TVs. A previous study reports that the subjective brightness is proportional to the average of displayed pixel luminance levels. However, other parameters can affect the subjective brightness. Therefore, we conducted a subjective evaluation test by using specific test images to identify the factors that affect the perceived overall brightness of HDR images. Our results indicated that positions and distributions of displayed pixel luminance levels on video affect brightness in addition to the average of displayed pixel luminance levels. The study is expected to contribute to the development of an objective metric for subjective brightness.