In this paper, we propose a novel perceptual-based intra coding optimization algorithm for the High Efficiency Video Coding (HEVC) using deep convolution networks (DCNs). According to the saliency map, the algorithm can intelligently adjust bit rate allocation between the salient and non-salient regions of the video. The proposed strategy mainly consists of two techniques, saliency map extraction, and intelligent bit rate allocation. First, we train a DCN model to generate the saliency map that highlights semantically salient regions. Compared with the texture-based region of interest (ROI) extraction techniques, our model is more consistent with the human visual system (HVS). Second, based on the saliency map, a modified rate-distortion optimization (RDO) method is designed to adaptively adjust bit rate allocation. As a result, the quality of the salient regions will be improved by allocating more bits while allocating fewer bit rates for the non-salient regions. The experimental results demonstrate that our approach can deal with multiple types of video to enhance the visual experience. For conventional videos, the proposed method achieves 0.64-dB PSNR improvement for the salient regions and saves 3.02% bit rate on average compared with HM16.7. Moreover, for conversational videos, the proposed method can significantly reduce the bit rate by 8.65% without dropping the quality of important regions.
A fundamental task in robotics is to plan collision-free motions among a set of obstacles. Recently, learning-based motion-planning methods have shown significant advantages in solving different planning problems in high-dimensional spaces and complex environments. This article serves as a survey of various different learning-based methods that have been applied to robot motion-planning problems, including supervised, unsupervised learning, and reinforcement learning. These learning-based methods either rely on a human-crafted reward function for specific tasks or learn from successful planning experiences. The classical definition and learning-related definition of motion-planning problem are provided in this article. Different learning-based motion-planning algorithms are introduced, and the combination of classical motion-planning and learning techniques is discussed in detail.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Abstract-We present a data-driven approach that colorizes 3D furniture models and indoor scenes by leveraging indoor images on the internet. Our approach is able to colorize the furniture automatically according to an example image. The core is to learn image-guided mesh segmentation to segment the model into different parts according to the image object. Given an indoor scene, the system supports colorization-by-example, and has the ability to recommend the colorization scheme that is consistent with a user-desired color theme. The latter is realized by formulating the problem as a Markov random field model that imposes user input as an additional constraint. We contribute to the community a hierarchically organized image-model database with correspondences between each image and the corresponding model at the part-level. Our experiments and a user study show that our system produces perceptually convincing results comparable to those generated by interior designers.
Lane detection is very important for self-driving vehicles. In recent years, computer stereo vision has been prevalently used to enhance the accuracy of the lane detection systems. This paper mainly presents a multiple lane detection algorithm developed based on optimised dense disparity map estimation, where the disparity information obtained at time tn is utilised to optimise the process of disparity estimation at time tn+1 (n ≥ 0). This is achieved by estimating the road model at time tn and then controlling the search range for the disparity estimation at time tn+1. The lanes are then detected using our previously published algorithm, where the vanishing point information is used to model the lanes. The experimental results illustrate that the runtime of the disparity estimation is reduced by around 37% and the accuracy of the lane detection is about 99%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.