Temporal Filter Design for Encoder-Oriented Video Generation Based on Bayesian Optimization

The acquisition rate of video equipment is advancing rapidly, for example, a video with Full-HD resolution can now be acquired at 1000 Hz. However, the acquisition process of imaging systems is independent from the video encoding process. The imaging system fails to utilize signals acquired at high temporal resolution in an attempt to improve video coding efficiency. This paper proposes a video generation algorithm that uses temporally over-sampled frames as input to produce a temporally down-sampled video signal optimized in terms of video encoding efficiency. The proposed method introduces an adaptive temporal filter whose filter coefficients are selected from adaptively generated set (called a dictionary) of candidates. As an extension of our previous work that studies filter coefficient selection from a fixed dictionary, the proposal of this paper designs the dictionary through Bayesian optimization. The proposed method makes it possible to jointly optimize the filter coefficient selection and the dictionary construction. Experiments show that the proposed method can reduce the encoding rate on average by 4.79 [%] compared to the constant mean-filter.