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Blind video quality assessment (BVQA) plays an indispensable role inmonitoring and improving the end-users' viewing experience in variousreal-world video-enabled media applications. As an experimental field, theimprovements of BVQA models have been measured primarily on a few human-ratedVQA datasets. Thus, it is crucial to gain a better understanding of existingVQA datasets in order to properly evaluate the current progress in BVQA.Towards this goal, we conduct a first-of-its-kind computational analysis of VQAdatasets via designing minimalistic BVQA models. By minimalistic, we restrictour family of BVQA models to build only upon basic blocks: a video preprocessor(for aggressive spatiotemporal downsampling), a spatial quality analyzer, anoptional temporal quality analyzer, and a quality regressor, all with thesimplest possible instantiations. By comparing the quality predictionperformance of different model variants on eight VQA datasets with realisticdistortions, we find that nearly all datasets suffer from the easy datasetproblem of varying severity, some of which even admit blind image qualityassessment (BIQA) solutions. We additionally justify our claims by contrastingour model generalizability on these VQA datasets, and by ablating a dizzyingset of BVQA design choices related to the basic building blocks. Our resultscast doubt on the current progress in BVQA, and meanwhile shed light on goodpractices of constructing next-generation VQA datasets and models.

Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models