Colour changes of cultural heritage objects can be related with degradation of materials, thus a proper colour monitoring system can be used to detect conservation problems. With this purpose, a monitoring methodology for cultural heritage preventive conservation based on tailored colour reference charts and image analysis is proposed. Reference colour charts have been designed and tested for use in museums. Charts containing 64 colour patches have been printed using high-stability inks on 4 different substrates: Acid-free paper SkyLight, Acid-free paper covered with a propylene film, FOREX® and GlassPack. The stability has been studied by accelerated ageing in an UV chamber, and the harmlessness of the materials by Oddy Test. The final selection of material, laminated paper, is a balance between the colour change upon ageing and the performance in the Oddy Test. Using this material and the proposed design, colour change of copper and silver coupons has been assessed using images that are adjusted and calibrated by an adaptive calibration framework employing a given set of reference colours which homogenises the visual information in the supplied images. Thus, regardless of the camera of origin, any processed picture will deliver reliable information of the state of the colour in the metal surfaces at the moment it was taken. Results demonstrate the adequacy of the approach and the design for colour calibration, so these charts can be used to monitor colour change of sensitive materials –metal coupons– using photographs. As colour change of reference metals is a consequence of corrosion by environmental factors this may be used as a measure of air quality in museum environments. This methodology can be used to design a low-cost preventive conservation tool, where colour change of metal coupons –or other reference materials– can be followed through image analysis of pictures taken periodically by conservators or visitors, introducing citizen science in the conservation strategy.

In recent years, the use of unmanned aerial vehicles (UAVs) for surveillance tasks has increased considerably. This technology provides a versatile and innovative approach to the field. However, the automation of tasks such as object recognition or change detection usually requires image processing techniques. In this paper we present a system for change detection in video sequences acquired by moving cameras. It is based on the combination of image alignment techniques with a deep learning model based on convolutional neural networks (CNNs). This approach covers two important topics. Firstly, the capability of our system to be adaptable to variations in the UAV flight. In particular, the difference of height between flights, and a slight modification of the camera’s position or movement of the UAV because of natural conditions such as the effect of wind. These modifications can be produced by multiple factors, such as weather conditions, security requirements or human errors. Secondly, the precision of our model to detect changes in diverse environments, which has been compared with state-of-the-art methods in change detection. This has been measured using the Change Detection 2014 dataset, which provides a selection of labelled images from different scenarios for training change detection algorithms. We have used images from dynamic background, intermittent object motion and bad weather sections. These sections have been selected to test our algorithm’s robustness to changes in the background, as in real flight conditions. Our system provides a precise solution for these scenarios, as the mean F-measure score from the image analysis surpasses 97%, and a significant precision in the intermittent object motion category, where the score is above 99%.

Monitoring environmental factors such as pollutants, relative humidity, light, etc. is needed to ensure a proper preventive conservation of cultural heritage. Since existing systems are expensive and difficult to maintain, we propose as a sustainable alternative the use crowdsourced photographs taken by visitors using their cell phones. These images of a tailor made panel, including sensitive materials and a calibration color chart, are calibrated and colorimetric information extracted from them. The aim of this paper is assessing the robustness of the color chart and the calibration method used, and evaluating how the ageing of the materials of the chart can affect the accuracy of the calibration process. A choice is made between four candidate materials and a set of industrial inks. Several tests have been performed and mathematical metrics extracted, so the most suitable material is adequately selected. It has been checked how the image calibration process, employed for homogenizing information from the crowdsourced pictures, performs with real-life materials and colors to assess the possible degradation that may happen. This work is a part of a bigger project with the aim of building a crowdsourcing-based monitoring system for preventive conservation of cultural heritage.

No-Reference video quality assessment has become a trending and challenging hot topic in estimating perceived quality in audiovisual content. In this paper, we present a proposal to considerably reduce the computational cost of video processing without losing accuracy in QoE estimation. Tests have been performed using the Video-MOS SaaS solution, a hybrid NR-VQA solution based on perceptible video distortions and a machine learning approach. After exploring the spatial and temporal redundancy present in a video sequence, the final approach combines video metric feature extraction in both high and low video resolution, together with a specific frame selection based on a uniform temporal sampling and frame type at the video coding level. An extensive validation with more than 144 hours of audiovisual content from six of the most important HD channels of DTT in Spain demonstrates the validity of the approach, ensuring real-time application on the test device, with computational cost savings of 94.96% and an obtained MOS error of 0.1144, in more than 174000 3-second measurements.