Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Deep sea video quality objective assessment model based on spatial-temporal feature

A technology for video quality and objective evaluation, applied in TV, electrical components, image communication, etc., to improve work efficiency

Active Publication Date: 2018-06-29
SHANGHAI OCEAN UNIV
View PDF7 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there is no report on this video quality assessment model.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Deep sea video quality objective assessment model based on spatial-temporal feature
  • Deep sea video quality objective assessment model based on spatial-temporal feature
  • Deep sea video quality objective assessment model based on spatial-temporal feature

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0073] The specific embodiments provided by the present invention will be described in detail below in conjunction with the accompanying drawings.

[0074] Please refer to figure 1 , figure 1 It is a flow chart of an objective evaluation model of deep-sea video quality based on spatio-temporal features of the present invention. A deep-sea video quality objective evaluation model based on spatio-temporal features, the deep-sea video quality objective evaluation model of spatio-temporal features comprises the following steps:

[0075] Step S1: Time-domain learning is performed on the deep-sea video sample set, and feature vectors based on the time-domain dimension are extracted.

[0076] The present invention learns the spatial information characteristics of the deep-sea video frame based on the DCTNet structure, follows the three-level spectrum histogram pipeline: utilizes the principal component analysis (PCA) to learn the multi-level filter bank, then performs nonlinear bin...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a deep sea video quality objective assessment model based on a spatial-temporal feature. The assessment model comprises the steps as follows: S1, a deep sea video sample set performs time domain learning, and extracts a feature vector based on a time domain dimension; S2, the deep sea video sample set performs spatial domain learning, and extracts a feature vector based ona spatial domain dimension; S3, the feature of the time domain is integrated with the feature of the spatial domain to form a final deep sea video quality classification data set; S4, classificationof a deep sea video quality classifier is semi-supervised; and S5, the deep sea video quality objective assessment model is built. The model has the advantage that the deep sea video quality objectiveassessment model is built, and an objective quality assessment service is provided for the public. The effect of various underwater image / video enhancement algorithms applied to improving deep sea video quality is assessed, and the work efficiency based on deep sea video research is improved.

Description

technical field [0001] The present invention relates to the field of deep-sea video quality evaluation, in particular to an objective quality evaluation method for deep-sea video based on spatio-temporal feature learning. This solution comprehensively considers the quality of video captured in deep-sea complex environments and according to the subjective evaluation results of deep-sea video quality, establishes a scientifically useful A fast and accurate method for objective quality assessment of deep-sea videos based on semantic metrics. Background technique [0002] Image / video quality evaluation research has a history of decades, and a large number of subjective and objective evaluation indicators and methods have been established. Some methods have been standardized by ITU and are widely used in various aspects of video applications. In terms of the quality of underwater images / videos, a large number of studies have focused on underwater image enhancement and restoratio...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): H04N17/00
CPCH04N17/004
Inventor 宋巍黄冬梅魏新宇赵丹枫刘诗梦
Owner SHANGHAI OCEAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com