构建可复现的光场质量评估框架，揭示现有指标在新编码流程中的局限性。

Benchmarking immersive media coding solutions, especially in the standardization context, requires reliable and reproducible subjective quality assessment (QA) procedures, along with objective quality metrics that remain accurate across different distortion types. This paper presents a standardized workflow for light field QA, developed and deployed in the context of JPEG Pleno standardization activities, which integrates benchmark generation, a hybrid subjective evaluation, and objective metric analysis into a common workflow. The benchmark is designed to encompass not only traditional coding-only artifacts but also distortions that arise in processing pipelines in which light field encoding is accompanied with view synthesis and reconstruction techniques. A hybrid subjective method is proposed enabling fine-grained assessment by combining reference-anchored quality rating with targeted pairwise refinement in perceptually ambiguous regions. The reliability of subjective scores is verified using statistical consistency analyses between observers of two cohorts. Finally, a large set of objective metrics is systematically evaluated in terms of global prediction accuracy, local agreement in ambiguous quality regions, and robustness across distortion families. The results show that several metrics achieve strong agreement for coding-only stimuli, but their performance consistently drops when view synthesis distortions are included. The analysis further highlights the importance of view-pooling strategy in the design of future light field quality metrics. The work provides a reproducible and standardization-ready framework for fine-grained light field QA, while identifying key limitations of current objective metrics under emerging coding pipelines. The subjectively annotated dataset is publicly available at https://plenodb.jpeg.org/lfqa/objectivecfp.