"Digital transformation and enhancement of the cultural heritage of ASFA, 1837 - 2021"
2023
[Research project]
"Athens School of Fine Arts - Digital Transformation and Promotion of the Cultural Heritage of ASFA, 1837 - 2021: A Collection of Works for the Study of Visual Evolution and the Historical Path of Modern Greek Art"
Participant in the research project "Digital Transformation and Promotion of the Cultural Heritage of ASFA, 1837 - 2021: A Collection of Works for the Study of Visual Evolution and the Historical Path of Modern Greek Art" with the code (MIS) 5093223, within the framework of the Operational Programme "Competitiveness, Entrepreneurship & Innovation 2014-2020."
Description of responsibilities:
Processing and upgrading digital audiovisual material from the archive of ASFA, including three hundred (300) video art works and one hundred (100) 3D graphics works, based on the specifications of the National Documentation Centre (Specifications for Digitization and Digital Archives).
Procedure
Testing & Evaluation Process:
Testing processing models, previewing, and output settings.
Video Enhancement entails a systematic approach to identify and apply specialized algorithms to specific regions within a video sequence, aiming to improve the overall visual quality. This process involves a thorough analysis of the video content, including frame-by-frame evaluation, to pinpoint areas where enhancement techniques can be most beneficial.
The identification stage involves visual inspection and computational analysis to detect and isolate problematic areas, such as:
Noise: Identifying and mitigating noise artifacts, which can manifest as grainy textures, speckles, or unwanted pixellation.
De-moire Reduction: Suppressing moiré patterns, which are repetitive shimmering or wavy artifacts caused by interference between grid patterns in the video source and the processing algorithms.
Sharp Edge Enhancement: Enhancing the definition and clarity of edges and boundaries within the video frames, improving the perceived sharpness and detail.
Scan Lines Reduction: Eliminating or reducing scan lines, which are horizontal artifacts typically associated with low-resolution video formats or interlaced video encoding.
Compression Artifact Removal: Counteracting compression artifacts, which are introduced during the video compression process and manifest as blockiness, blurring, or color banding.
Once these problematic areas are identified, the appropriate algorithmic processing models are applied. These models can be categorized into several types:
Spatial Filters: These filters operate on individual frames, analyzing the spatial relationships between pixels to enhance sharpness, reduce noise, or remove artifacts.
Temporal Filters: These filters consider the temporal context of the video, analyzing motion vectors and temporal patterns to smooth out transitions, reduce flicker, and improve temporal consistency.
Learning-Based Techniques: These techniques utilize machine learning or deep learning algorithms to learn from a training dataset of high-quality video frames and apply their knowledge to enhance lower-quality video content. The combination of these algorithmic models and the strategic application of enhancement techniques at specific points in the video sequence leads to a significant improvement in overall video quality. This process is crucial for preserving the visual integrity of the archival and historical video footage, enhancing the viewing experience for audiences, and enabling enhanced analysis and interpretation of video data.
* The project is nearing completion and is in the final stages of delivery.
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