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Quality assessment of images and videos emphasizes both local details andglobal semantics, whereas general data sampling methods (e.g., resizing,cropping or grid-based fragment) fail to catch them simultaneously. To addressthe deficiency, current approaches have to adopt multi-branch models and takeas input the multi-resolution data, which burdens the model complexity. In thiswork, instead of stacking up models, a more elegant data sampling method (namedas SAMA, scaling and masking) is explored, which compacts both the local andglobal content in a regular input size. The basic idea is to scale the datainto a pyramid first, and reduce the pyramid into a regular data dimension witha masking strategy. Benefiting from the spatial and temporal redundancy inimages and videos, the processed data maintains the multi-scale characteristicswith a regular input size, thus can be processed by a single-branch model. Weverify the sampling method in image and video quality assessment. Experimentsshow that our sampling method can improve the performance of currentsingle-branch models significantly, and achieves competitive performance to themulti-branch models without extra model complexity. The source code will beavailable at https://github.com/Sissuire/SAMA.

Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment