Automatizing chromatic quality assessment for cultural heritage image digitization

Abstract

In the context of digitization of photographs and other documents with graphical value, cultural heritage organizations need to give a guarantee that the stored digital image is a faithful representation of the physical image both at the physical level and the perceptual level. On the physical level, image quality can be measured objectively in a simple way by applying certain physical attributes to the image, as well as by measuring how distorting images affects the performance of the attributes. However, on the perceptual level, image quality should correspond to the perception that a human expert would experience when observing the physical image under certain determined and controlled conditions. In this paper we address the problem of image quality assessment (IQA) in the context of cultural heritage digitization by applying machine learning (ML). In particular, we explore the possibility of creating a decision tree that mimics the response of an expert on cultural heritage when observing cultural heritage images.

Statistical Model for the Quality of Panoramic Images of 2D artifacts

“…Figure 2 illustrates how to create panoramic images by stitching a series of overlapped image components. Image quality (IQ ) models which is set up to quantify the overall image quality usually consist of a sub-set of quality attributes (QA) [31][32][33][34][35][36]. Therefore, the number of selected QAs is a very important step.…”

“…So, selecting the most accurate set of QA will be the crucial task in this scenario. Accordingly, Authors researched on a set of image attributes that would contribute a quality panoramic image output [2,31,[35][36][37]. It was well studied the underline theory of creating panoramic images, called image stitching.…”

Fields Touched by Digitalization: Analysis of Scientific Activity in Scopus

“…In computer programs, an algorithm consists of the first stage for writing code. This topic includes research entitled "Automatizing Chromatic Quality Assessment for Cultural Heritage Image Digitization" carried out by [57], which dealt with an obstacle in the evaluation of image quality (IQA) in reference to the digitization of cultural heritage, resorting to machine learning (ML). The authors analyzed the option of designing a decision tree that replicates the result of a specialist when visualizing images of cultural assets.…”

Conserving knowledge heritage: opportunities and challenges in conceptualizing cultural heritage information system (CHIS) in the Indian context

“…Any information, which possesses the contents of digitized materials, needs to ensure the quality of the process of digitization and the tool used for storing such contents. Further with regard to cultural heritage items, assessing the quality of the digitized images has become crucial to store faithful representations of the originals (Granados et al , 2019). Digitization of documents and other cultural heritage materials uses document management systems, software tools and digital technologies to perform digitization and to create digital archives.…”