Image clarity is a fundamental attribute in film and television systems, closely related to the preservation of spatial detail throughout acquisition, compression, transmission, and post-processing pipelines. In practical engineering workflows, image degradation arises from multiple sources, including optical defocus, motion blur, and lossy compression, each progressively altering spatial structure and reducing detail. Such spatial detail is equally critical in industrial imaging tasks, such as textile surface analysis, where clarity impacts the resolution of fine fiber structures. Objective image quality assessment (IQA) metrics are widely employed to characterize such degradation due to their reproducibility and computational efficiency. However, the numerical response behavior of these metrics under clarity-related degradation has not been sufficiently examined from a metric-domain engineering perspective, as existing research primarily focuses on perceptual correlation rather than systematic numerical behavior analysis. This paper presents an engineering-oriented analysis framework for film and television image clarity based on objective IQA metrics. Rather than modeling perceptual clarity or relying on subjective opinion scores, the proposed approach characterizes how metric outputs respond numerically to explicitly controlled clarity-related degradations. Metric behavior is analyzed along three complementary dimensions: monotonicity with respect to degradation strength, numerical sensitivity to incremental parameter changes, and stability across diverse content. Experiments are conducted under Gaussian blur, motion blur, and compression-induced degradation scenarios using a standardized film and television dataset. The results characterize degradation-dependent and content-dependent response patterns of commonly used full-reference (FR) and no-reference (NR) metrics. Compared with existing analysis methods, the proposed framework avoids reliance on perceptual modeling and provides a reproducible, interpretable basis for engineering tasks such as metric selection, parameter tuning, and system optimization across film, television, and related industrial imaging fields.

film and television image clarity, objective image quality assessment, degradation analysis, metric response behavior, industrial texture imaging