134,917 research outputs found
Hybrid Video Coding based on Bidimensional Matching Pursuit
Hybrid video coding combines together two stages: first, motion estimation and compensation predict each frame from the neighboring frames, then the prediction error is coded, reducing the correlation in the spatial domain. In this work, we focus on the latter stage, presenting a scheme that profits from some of the features introduced by the standard H.264/AVC for motion estimation and replaces the transform in the spatial domain. The prediction error is so coded using the matching pursuit algorithm which decomposes the signal over an appositely designed bidimensional, anisotropic, redundant dictionary. Comparisons are made among the proposed technique, H.264, and a DCT-based coding scheme. Moreover, we introduce fast techniques for atom selection, which exploit the spatial localization of the atoms. An adaptive coding scheme aimed at optimizing the resource allocation is also presented, together with a rate-distortion study for the matching pursuit algorithm. Results show that the proposed scheme outperforms the standard DCT, especially at very low bit rates
Semi-hierarchical based motion estimation algorithm for the dirac video encoder
Having fast and efficient motion estimation is crucial in today’s advance video compression
technique since it determines the compression efficiency and the complexity of a video encoder. In this paper, a method which we call semi-hierarchical motion estimation is proposed for the Dirac video encoder. By considering the fully hierarchical motion estimation only for a certain type of inter frame encoding, complexity
of the motion estimation can be greatly reduced while maintaining the desirable accuracy. The experimental results show that the proposed algorithm gives two to three times reduction in terms of the number of SAD calculation compared with existing motion estimation algorithm of Dirac for the same motion estimation
accuracy, compression efficiency and PSNR performance. Moreover, depending upon the complexity of the test sequence, the proposed algorithm has the ability to increase or decrease the search range in order to maintain the accuracy of the motion estimation to a certain level
Low computational complexity variable block size (VBS) partitioning for motion estimation using the Walsh Hadamard transform (WHT)
Variable Block Size (VBS) based motion estimation has
been adapted in state of the art video coding, such as
H.264/AVC, VC-1. However, a low complexity H.264/AVC
encoder cannot take advantage of VBS due to its power consumption
requirements. In this paper, we present a VBS partition
algorithm based on a binary motion edge map without
either initial motion estimation or Rate-Distortion (R-D)
optimization for selecting modes. The proposed algorithm
uses the Walsh Hadamard Transform (WHT) to create a binary
edge map, which provides a computational complexity
cost effectiveness compared to other light segmentation
methods typically used to detect the required region
Vision and Learning for Deliberative Monocular Cluttered Flight
Cameras provide a rich source of information while being passive, cheap and
lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work
we present the first implementation of receding horizon control, which is
widely used in ground vehicles, with monocular vision as the only sensing mode
for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a
number of contributions: novel coupling of perception and control via relevant
and diverse, multiple interpretations of the scene around the robot, leveraging
recent advances in machine learning to showcase anytime budgeted cost-sensitive
feature selection, and fast non-linear regression for monocular depth
prediction. We empirically demonstrate the efficacy of our novel pipeline via
real world experiments of more than 2 kms through dense trees with a quadrotor
built from off-the-shelf parts. Moreover our pipeline is designed to combine
information from other modalities like stereo and lidar as well if available
Tightly Coupled 3D Lidar Inertial Odometry and Mapping
Ego-motion estimation is a fundamental requirement for most mobile robotic
applications. By sensor fusion, we can compensate the deficiencies of
stand-alone sensors and provide more reliable estimations. We introduce a
tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing
the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO)
can perform well with acceptable drift after long-term experiment, even in
challenging cases where the lidar measurements can be degraded. Besides, to
obtain more reliable estimations of the lidar poses, a rotation-constrained
refinement algorithm (LIO-mapping) is proposed to further align the lidar poses
with the global map. The experiment results demonstrate that the proposed
method can estimate the poses of the sensor pair at the IMU update rate with
high precision, even under fast motion conditions or with insufficient
features.Comment: Accepted by ICRA 201
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