Abstract

Assessing the quality of colorized images remains challenging as most colorization artifacts arise from high-level semantic errors in the form of implausible or unnatural color assignments, rather than conventional low-level distortions such as noise, blur, or compression artifacts. The evaluation process is further complicated by the inherently subjective nature of color perception and the difficulty of accurately modeling human responses to color. Motivated by the limitations of current image quality assessment metrics for this task, we propose TRIPSI (Triple-Source Realigned Integrated Perceptual Semantic Index), a hybrid full-reference framework which approaches colorization quality assessment as a pattern recognition problem in a learned semantic-aware feature space. TRIPSI fuses three complementary deep pre-trained models, TOPIQ, LIQE, and DreamSim, into a unified framework by applying rank normalization to individual model scores per dataset to ensure comparability across varying output scales before aggregating the scores with equal weights. LIQE captures explicit color distortions. TOPIQ focuses on semantically important regions and color saturation artifacts. DreamSim measures color-preserving pattern agreement between deep feature representations of a colorized image and its ground-truth reference color image in a learned semantic-aware embedding space. By explicitly incorporating color-aware semantic representations at multiple levels, results across multiple datasets show that TRIPSI closely reflects human perceptual judgments, highlighting the effectiveness of semantic pattern modeling for quality assessment in image colorization.

IPC Classification

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