Adaptive Motion Vector Smoothing for Improving Side Information in Distributed Video Coding

Guo, Jun; Kim, Joohee

doi:10.3745/jips.2011.7.1.103

Cited by 10 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of the proposed LMS mean smoothing algorithm is validated first. Figure 9 shows, respectively: a typical scenario when carrying out MFE with no smoothing; with the LMS smoothing presented in [ 33 ] (represented by LMS-1), with the LMS smoothing proposed in this paper (represented by LMS-2) and with the proposed LMS mean smoothing.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR

Zhang

Mao

et al. 2014

Sensors

View full text Add to dashboard Cite

This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively.

show abstract

Section: Methodsmentioning

confidence: 99%

“…An improved least mean square-based spatial smoother (LMS-smoothing) ([ 32 , 33 ]) is first applied to optimize the motion field. The velocity vector of a moving grid g 0 is denoted by v 0 , the velocity vector of its neighboring cell by v i and the number of its neighboring cells by N .…”

Section: Motion Field Estimationmentioning

confidence: 99%

Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR

Zhang

Mao

et al. 2014

Sensors

View full text Add to dashboard Cite

show abstract

“…First, we remove clutter using a median filter for target detection. Next, closing and opening operations are performed by using various sized structure elements, and target candidate pixels are obtained by means of a subtraction operation between the closing and opening operations.…”

Section: Introductionmentioning

confidence: 99%

Small target detection using morphology and modified Gaussian distance function

Kim

Park

Ahn

et al. 2014

Security Comm Networks

View full text Add to dashboard Cite

We propose a new small target detection system that detects small target candidates based on morphology operations and detects actual targets using a modified Gaussian distance function. To reduce clutter on the edges of clouds, a median filter is applied as preprocessing. Two kinds of images are calculated with closing and opening morphological operators, respectively. In the morphology operations, various sizes of structure elements that are used to consider the sizes of targets and candidate targets are extracted from different images between the two images in the closing and opening operations. With a modified Gaussian distance function, small targets are detected from the candidate targets. The proposed method is less sensitive to clutters than existing methods and has a detection rate of 98%. Copyright © 2014 John Wiley & Sons, Ltd.

show abstract

“…In many multimedia services, image compression techniques reduce data size without hampering image quality, which enables the transmission and the storage of image data with fewer resources [1,2]. From among such techniques, H.264 [3] is one of the recent image compression standards and has been used for many digital image devices, such as monitoring cameras, camcorders, and mobile phones.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Image Backtrack-Based Fast Direct Mode Decision Algorithm

Choi

Park

2012

Journal of Information Processing Systems

View full text Add to dashboard Cite

B frame bi-directional predictions and the DIRECT mode coding of the H.264 video compression standard necessitate a complex mode decision process, resulting in a long computation time. To make H.264 feasible, this paper proposes an image backtrackbased fast (IBFD) algorithm and evaluates the performances of two promising fast algorithms (i.e., AFDM and IBFD). Evaluation results show that an image backtrackbased fast (IBFD) algorithm can determine DIRECT mode macroblocks with 13% higher accuracy, as compared with the AFDM. Furthermore, IBFD is shown to reduce the motion estimation time of B frames by up to 23% with a negligible quality degradation

show abstract

Adaptive Motion Vector Smoothing for Improving Side Information in Distributed Video Coding

Cited by 10 publications

References 11 publications

Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR

Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR

Small target detection using morphology and modified Gaussian distance function

Evaluation of the Image Backtrack-Based Fast Direct Mode Decision Algorithm

Contact Info

Product

Resources

About