Transcript
Page 1: BLeSS: Bio-inspired Low-level Spatiochroamtic Similarity Assisted Image Quality … › 2016 › 10 › ... · 2017-03-08 · BLeSS: Bio-inspired Low-level Spatiochroamtic Similarity

BLeSS:

Bio-inspired Low-level Spatiochroamtic Similarity Assisted Image Quality Assessment

School of Electrical and Computer Engineering, Center for Signal and Information Processing

DoganCan Temel and Ghassan AlRegib

Georgia Institute of Technology, USA

CENTER SURROUND EFFECTS

MOTIVATION

CONTACT US

[email protected] [email protected] cantemel.com ece.gatech.edu/research/labs/msl/

E M A I L W E B

Application Average daily

shared photos

390 Million

700 Million

70 Million

760 Million

[1]

Billions of images are shared every day.

Supported resolution: not very high

Supported resolution by display systems:

up to ultra HD

Applications started to catch up with

high resolution.

Users are concerned about network usage

To balance quality and network usage, image

optimization requires image quality assessmentRelated Applications

Smart Capture Remote Assistance

PROBLEM DESCRIPTION

Reference images

Distorted images

Subjective ScoresBad1

very

annoying

Poor2 annoying

Fair3slightly

annoying

Good4distortion but not

annoying

Very

Good5no perceived

distortion

Test setup Stimuli

Problem Model

QUALITY

ESTIMATORSFidelity Structure

Scale

Space

Visual

SystemPooling Color

Before

2000

MSE-PSNR

PSNRc Pixel-wise

2000 NQM

2003 MS-SSIM

2004 SSIM

2006 PSNR-HVS

2007PSNR-HVS-M

VSNR

2008VIF

C4 Local Mean

2009CW-SSIM

Li-Wang

2011

PSNR-HA

PSNR-HMA

FSIM

FSIMc Pixel-wise

IW-SSIM

LBIQ

DIIVINE

2012

CIEDE Local Mean

BRISQUE

CORNIA

MLIQM

SR-SIM

2013

CB/SF

QAC

SPARQ

2014

Tang

QAF

Kang

𝑄𝑎𝑟𝑒𝑎

𝑄𝑒𝑥𝑝𝑜𝑛𝑒𝑛𝑡

2015

REDLOG

IQA-CNN++

Li

𝑆2𝐹2

DLIQA

Gao

2016

CNN-SVR

BLeSSCenter

Surround

LITERATURE

SSF is high stimuli ~ surround

SSF is low stimuli !~ surround

Surround Spatial Frequency (SSF)

Surround Orientation

BLeSS PIPELINE

VALIDATIONVISUALIZATION

Distorted ImageReference Image

SR-SIM Map FSIM Map BLeSS Maps

Percentage performance (Spearman correlation)

changes for BLeSS-assisted IQA methods over

distortion categories.

Percentage performance (Spearman correlation)

changes for BLeSS-assisted IQA methods over

full databases.

Spatiochromatic Grouping Pipeline

BLeSS Pipeline

Murray 2013

Murray 2013

SR-SIM FSIM FSIMc

Comp. -0.29 (000) +0.13 (000) +0.28 (000)

Noise -2.16 (001) -1.31 (000) -0.34 (000)

Comm. +0.07 (0-0) +0.25 (0-0) +0.24 (0-0)

Blur -0.39 (000) +0.20 (000) +0.40 (000)

Color +183 (--1) +185 (--1) +13.1 (--1)

Global -1.31 (--0) -4.69 (--0) -0.16 (--0)

Local -1.85 (--0) +4.36 (--0) +3.12 (--0)

SR-SIM FSIM FSIMc

LIVE +0.13 (0) +0.17 (0) +0.06 (0)

MULTI -0.33 (0) +0.62 (0) +0.79 (0)

TID13 +3.79 (1) +4.77 (1) +1.03 (0)

LIVE MULTI TID13 Total

Comp. 460 180 375 1015

Noise 174 180 1375 1729

Comm. 174 - 250 424

Blur 174 315 250 739

Color - - 375 375

Global - - 250 250

Local - - 250 250

The number of distorted images with respect to

degradation categories in each database.

Wavelet

Transform

Grouplet

Transform

Center

Contrast

Normalization

Contrast

Sensitivity

Adjustment

Bicubic

Interpolation

Inverse

Wavelet

Transform𝜏𝑖

Opponent

Color Channel

Separation

Euclidean

Norm𝑖 ∈ 1, 2, 3𝜏𝐼𝑖

𝑖 ∈ 1, 2, 3𝐼

Spatiochromatic

Grouping

Pixel-wise

Similarity

𝐼

Spatiochromatic

Grouping𝐼̃

Mean

PoolingBLeSS

LIVE

LIVE

𝜏

𝜏̃

Top Related