Gaussian Fourier Pyramid for Local Laplacian Filter

Multi-scale processing is essential in image processing and computer graphics. Halos are a central issue in multi-scale processing. Several edge-preserving decompositions resolve halos, e.g., local Laplacian filtering (LLF), by extending the Laplacian pyramid to have an edge-preserving property. Its processing is costly; thus, an approximated acceleration of fast LLF was proposed to linearly interpolate multiple Laplacian pyramids. This paper further improves the accuracy by Fourier series expansion, named Fourier LLF. Our results showed that Fourier LLF has a higher accuracy for the same number of pyramids. Moreover, Fourier LLF exhibits parameter-adaptive property for content-adaptive filtering. The code is available at: https://norishigefukushima.github.io/GaussianFourierPyramid/.

On the Performance Evaluation of Action Recognition Models on Transcoded Low Quality Videos

In the design of action recognition models, the quality of videos in the dataset is an important issue, however the trade-off between the quality and performance is often ignored. In general, action recognition models are trained and tested on high-quality videos, but in actual situations where action recognition models are deployed, sometimes it might not be assumed that the input videos are of high quality. In this study, we report qualitative evaluations of action recognition models for the quality degradation associated with transcoding by JPEG and H.264/AVC. Experimental results are shown for evaluating the performance of pre-trained models on the transcoded validation videos of Kinetics400. The models are also trained on the transcoded training videos. From these results, we quantitatively show the degree of degradation of the model performance with respect to the degradation of the video quality.