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Imprecision and noise in the time series data may result in series with similar overall behaviors being recognized as being dissimilar because of the accumulation of many small local differences in noisy observations. While smoothing techniques can be used for eliminating such noise, the degree of smoothing that needs to be performed may vary significantly at di erent parts of the given time series. In this paper, we propose a content-adaptive smoothing, CA-Smooth, technique to reduce the impact of non-informative details and noise in time series by means of a data-driven approach to smoothing. The proposed smoothing process treats different parts of the time series according to local information content. We show the impact of di erent adaptive smoothing criteria on a number of samples from different datasets, containing series with diverse characteristics.

Aiming at the problem of choosing high-pass filters in image processing layer of deep CNN (Convolution Neural Network) for steganalysis, this paper studies the effect of different filter banks on detection performance for content-adaptive JPEG steganography, and proposes a steganalysis method based on multi-scale 2D Gabor filtering and ensemble of multiple deep CNNs. Firstly, the effect of high-pass filters of image processing layer on detection performance is studied and the detection errors corresponding to the different combinations of high-pass filters are shown, then the relevant experimental results are analyzed. Lastly, two typical content-adaptive JPEG steganography algorithms such as UED and J-UNIWARD are taken as examples, the proposed steganalysis method is compared with other methods to verify the effectiveness of the proposed method.

Cinematic virtual reality provides an immersive visual experience by presenting omnidirectional videos of real-world scenes. A key challenge is to develop efficient representations of omnidirectional videos in order to maximize coding efficiency under resource constraints, specifically, number of samples and bitrate. We formulate the choice of representation as a multi-dimensional, multiple-choice knapsack problem and show that the resulting representations adapt well to varying content. We also show that separation of the sampling and bit allocation constraints leads to a computationally efficient solution using Lagrangian optimization with only minor performance loss. Results across images and videos show significant coding gains over standard representations.

This paper presents a content-adaptive system for streaming reconstructed soccer goal events over networks with bandwidth limited to 1.5Mbps or below. The reconstruction module analyzes a soccer video to produce corresponding panoramic field model with localized motion trajectories to provide tactic analysis for user's better comprehension of the goal events. The transmission module designs 3 schemes for different bandwidth conditions (150Kbps-1.5Mbps), (56-150Kbps), and (