"Estimation"

HEVC VMAF-oriented Perceptual Rate Distortion Optimization using CNN

Video coding standards like HEVC and VVC have achieved significant coding performance. However, the RDO module in coding framework ignores the characteristics of human visual system (HVS), which leads to insufficiency for perceptual video coding. …

Learning Based Estimation of Video Coding Distortion

Coding distortion is a critical factor in video coding algorithms such as rate distortion optimization, rate control and optimal quantization. Accurate distortion estimation without complex pre-coding has always been highly desired. Traditional …

Two-stream recurrent convolutional neural networks for video saliency estimation

Recently, research has emphasized the need for video saliency estimation since its application covers a large domain. Traditional saliency prediction methods for video based on hand-crafted visual features lead to slow speed and ineffective results. …

Review of ITU-T parametric models for compressed video quality estimation

This paper presents a review of parametric models for video quality estimation standardized in the past few years. The focus of this paper is the estimation of quality degradation caused by the compression artifacts. The models introduced in the …

Image restoration via efficient Gaussian mixture model learning

Expected Patch Log Likelihood (EPLL) framework using Gaussian Mixture Model (GMM) prior for image restoration was recently proposed with its performance comparable to the state-of-the-art algorithms. However, EPLL uses generic prior trained from …

Hybrid center-symmetric local pattern for dynamic background subtraction

Effective foreground detection in dynamic scenes is a challenging task in computer vision applications. In this paper, we propose a novel background modeling method to tackle this problem. First, we propose a second-order center-symmetric local …

Dynamic background subtraction based on spatial extended center-symmetric local binary pattern

Moving objects detection in dynamic scenes is a challenging task in many computer vision applications. Traditional background modeling methods do not work well in these situations since they assume a nearly static background. In this paper, a novel …