Image quality index of the monochrome archival photographs' compression

The recent process of digitalizing archives has increased the importance of choosing the best compression method and evaluating the quality of the compressed materials. Our paper focuses on monochrome photographs. We suggest a new image quality index partly based on Human Visual System. We think that, despite its simplicity, it is equal to Mean Subjective Rank. In addition, we intend to ascertain that (the index submitted by us) our index is very easy both to understand and to implement

A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets

[Abstract]: In optical coherence tomography (OCT), there is a trade-off between the scanning time and image quality, leading to a scarcity of high quality data. OCT platforms provide different scanning presets, producing visually distinct images, limiting their compatibility. In this work, a fully automatic methodology for the unpaired visual conversion of the two most prevalent scanning presets is proposed. Using contrastive unpaired translation generative adversarial architectures, low quality images acquired with the faster Macular Cube preset can be converted to the visual style of high visibility Seven Lines scans and vice-versa. This modifies the visual appearance of the OCT images generated by each preset while preserving natural tissue structure. The quality of original and synthetic generated images was compared using BRISQUE. The synthetic generated images achieved very similar scores to original images of their target preset. The generative models were validated in automatic and expert separability tests. These models demonstrated they were able to replicate the genuine look of the original images. This methodology has the potential to create multi-preset datasets with which to train robust computer-aided diagnosis systems by exposing them to the visual features of different presets they may encounter in real clinical scenarios without having to obtain additional data.Instituto de Salud Carlos III; DTS18/00136Ministerio de Ciencia e Innovación; RTI2018-095894-B-I00Ministerio de Ciencia e Innovación; PID2019-108435RB-I00Ministerio de Ciencia e Innovación; TED2021-131201B-I00Ministerio de Ciencia e Innovación; PDC2022-133132-I00Xunta de Galicia; ED431C 2020/24Xunta de Galicia; ED481A 2021/161Axencia Galega de Innovación; IN845D 2020/38Xunta de Galicia; ED481B 2021/059Xunta de Galicia; ED431G 2019/0

Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering

Humankind is entering a novel era of creativity - an era in which anybody can synthesize digital content. The paradigm under which this revolution takes place is prompt-based learning (or in-context learning). This paradigm has found fruitful application in text-to-image generation where it is being used to synthesize digital images from zero-shot text prompts in natural language for the purpose of creating AI art. This activity is referred to as prompt engineering - the practice of iteratively crafting prompts to generate and improve images. In this paper, we investigate prompt engineering as a novel creative skill for creating prompt-based art. In three studies with participants recruited from a crowdsourcing platform, we explore whether untrained participants could 1) recognize the quality of prompts, 2) write prompts, and 3) improve their prompts. Our results indicate that participants could assess the quality of prompts and respective images. This ability increased with the participants' experience and interest in art. Participants further were able to write prompts in rich descriptive language. However, even though participants were specifically instructed to generate artworks, participants' prompts were missing the specific vocabulary needed to apply a certain style to the generated images. Our results suggest that prompt engineering is a learned skill that requires expertise and practice. Based on our findings and experience with running our studies with participants recruited from a crowdsourcing platform, we provide ten recommendations for conducting experimental research on text-to-image generation and prompt engineering with a paid crowd. Our studies offer a deeper understanding of prompt engineering thereby opening up avenues for research on the future of prompt engineering. We conclude by speculating on four possible futures of prompt engineering.Comment: 29 pages, 10 figure

Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera

Conventional sleep monitoring is time-consuming, expensive and uncomfortable, requiring a large number of contact sensors to be attached to the patient. Video data is commonly recorded as part of a sleep laboratory assessment. If accurate sleep staging could be achieved solely from video, this would overcome many of the problems of traditional methods. In this work we use heart rate, breathing rate and activity measures, all derived from a near-infrared video camera, to perform sleep stage classification. We use a deep transfer learning approach to overcome data scarcity, by using an existing contact-sensor dataset to learn effective representations from the heart and breathing rate time series. Using a dataset of 50 healthy volunteers, we achieve an accuracy of 73.4\% and a Cohen's kappa of 0.61 in four-class sleep stage classification, establishing a new state-of-the-art for video-based sleep staging.Comment: Accepted to the 6th International Workshop on Computer Vision for Physiological Measurement (CVPM) at CVPR 2023. 10 pages, 12 figures, 5 table

Investigation of the quality of umbilical artery Doppler waveforms

In Doppler systems which automatically calculate the maximum frequency envelope and pulsatility index (PI) of umbilical artery Doppler waveforms there is the possibility of error in these parameters when the technical quality of the acquired waveform is low. Low quality waveforms may arise when there is an inappropriate set of physical parameters or when there are other sources of noise such as overlying vessels signals. In this thesis the effect of physical parameters on the envelope and on PI are investigated, and also methods for the detection of low quality waveforms are described and tested. A flow phantom which is able to produce realistic looking umbilical artery Doppler waveforms is described. This is based upon microcompruter control of a stepping motor / gear pump combination. The statistics of the Doppler spectra produced using artificial blood and human blood in the phantom are found to be identical. The effect of a number of physical parameters on the simulated umbilical artery waveforms produced using the phantom is investigated. The accuracy of estimation of the envelope and the PI is similar over a wide range of physical conditions. A suitable image processing algorithm for speckle reduction of Doppler waveforms is developed and tested using simulated waveforms from the phantom. Using the flow device it was found that both filtering of the envelope and also speckle suppression of the spectrum improved the accuracy of estimation of the envelope and of the PI. A number of quality indices based upon the degree of noise of the envelope are described. Using the flow device there is found to be a high correlation between the quality index values, and the errors in PI and errors in envelope estimation respectively. In a clinical trial the quality index values from umbilical arteries were compared with the waveform quality as assessed by a skilled observer. The clinical results show that quality indices are able to separate high and low quality waveforms when the indices are calculated from the unprocessed envelope, but not when calculated from the filtered envelop

Robust drift-free bit-rate preserving H.264 watermarking

International audienceThis paper presents a novel method for open-loop watermarking of H.264/AVC bitstreams. Existing watermarking algorithms designed for previous encoders, such as MPEG-2 cannot be directly applied to H.264/AVC, as H.264/AVC implements numerous new features that were not considered in previous coders. In contrast to previous watermarking techniques for H.264/AVC bitstreams, which embed the information after the reconstruction loop and perform drift compensation, we propose a completely new intra-drift-free watermarking algorithm. The major design goals of this novel H.264/AVC watermarking algorithm are runtime-efficiency, high perceptual quality, (almost) no bit-rate increase and robustness to re-compression. The watermark is extremely runtime-efficiently embedded in the compressed domain after the reconstruction loop, i.e., all prediction results are reused. Nevertheless, intra-drift is avoided, as the watermark is embedded in such a way that the pixels used for the prediction are kept unchanged. Thus, there is no drift as the pixels being used in the intra-prediction process of H.264/AVC are not modified. For watermark detection, we use a two-stage cross-correlation. Our simulation results confirm that the proposed technique is robust against re-encoding and shows a negligible impact on both the bit-rate and the visual quality

AdaFace: Quality Adaptive Margin for Face Recognition

Recognition in low quality face datasets is challenging because facial attributes are obscured and degraded. Advances in margin-based loss functions have resulted in enhanced discriminability of faces in the embedding space. Further, previous studies have studied the effect of adaptive losses to assign more importance to misclassified (hard) examples. In this work, we introduce another aspect of adaptiveness in the loss function, namely the image quality. We argue that the strategy to emphasize misclassified samples should be adjusted according to their image quality. Specifically, the relative importance of easy or hard samples should be based on the sample's image quality. We propose a new loss function that emphasizes samples of different difficulties based on their image quality. Our method achieves this in the form of an adaptive margin function by approximating the image quality with feature norms. Extensive experiments show that our method, AdaFace, improves the face recognition performance over the state-of-the-art (SoTA) on four datasets (IJB-B, IJB-C, IJB-S and TinyFace). Code and models are released in https://github.com/mk-minchul/AdaFace.Comment: to be published in CVPR2022 (Oral

Atmospheric Pollution Causes Deterioration of Sweeteners of Treats and Decreases Competitiveness in the Food Industry of Coastal Baja California, Mexico

The use of pigments and sweeteners in the food industry has been of great importance over the last thirty years, as an aid to obtaining better quality products of certain types of food that require specialized methods of production, such as sweet breads. Since pigments were first used they have increased the competitiveness of the regional, national and international bakeries and treats companies found on the coast of Baja California, in northwest Mexico. In this region of the country, many people consume a considerable quantity of sweet bread, principally in winter. The pigments are made using specialized methods and have qualities relevant to the special conditions required in the storage and manufacturing processes, as well as to the types of food and the packaging associated with them. This is done in order to achieve a favorable appearance, and to create an be aactive product fortoforonsumers. Any bread with defective sweetener or pigmentation levels must be rejected and returned to the manufacturing process or be offered as food to pigs at a much lower market price resulting in economic losses to the company and a decrease in competitiveness. A specialized analysis of the subject was undertaken between 2012 and 2014 with the use of Scanning Electron Microscopy (SEM)