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There is a strong and ever-growing demand for higher-resolution video content, such as UltraHD, which requires significantly higher bitrates. Providing such content at scale is a challenge due to limitations of the available last-mile bandwidth and content delivery network (CDN) storage and egress capacity. Lower bitrates are often considered an answer. This way, the high-resolution video content is often compressed with visually perceptible coding artifacts, thereby leading to an inferior user experience. Improved compression efficiency is thus the obvious solution for improving the user experience. However, in order to realize the gain from such efficiency improvements in a large-scale deployment, such improvements need to be applicable to an already deployed ecosystem such as set-top boxes or mobile devices, and SmartTVs, and to have a reasonably low computational complexity. This work proposes a low-complexity joint backward and forward temporal masking for reducing bitrate without perceptibly affecting visual quality. This is achieved by introducing a novel low-complexity scenecut-aware adaptive frame-level quantization framework, which considers temporal distances between frames and the closest scenecuts. The proposed framework has been implemented in the popular x265 open-source HEVC encoder. With that said, the framework is codec-independent and can be applied to other encoders and video coding standards. Different backward and forward masking time periods and quantizer behaviors are investigated to determine exact time periods for which temporal masking does not substantially impact video quality, as perceived by the human visual system (HVS). Extensive subjective quality assessments have been carried out for evaluating the benefits and advantages of the proposed scenecut-aware adaptive quantization framework. The subjective results showed significant bitrate savings of up to about 26%, while maintaining substantially the same perceived visual quality.
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