A NOVEL COMPRESSED IMAGES QUALITY METRIC

2011 ◽  
Vol 11 (02) ◽  
pp. 281-292
Author(s):  
WEN LU ◽  
LIHUO HE ◽  
WENJIAN TANG ◽  
FEI GAO ◽  
WEILONG HOU
Keyword(s):  
Image Quality ◽  
Quality Index ◽  
Image Quality Assessment ◽  
Performance Indicator ◽  
Directional Information ◽  
Quality Metric ◽  
Compressed Images ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Image Quality Index

As the performance indicator of the image processing algorithms or systems, image quality assessment (IQA) has attracted the attention of many researchers. Aiming to the widely used compression standards, JPEG and JPEG2000, we propose a new no reference (NR) metric for compressed images to do IQA. This metric exploits the causes of distortion by JPEG and JPEG2000, employs the directional discrete cosine transform (DDCT) to obtain the detail and directional information of the images and incorporates with the visual perception to obtain the image quality index. Experimental results show that the proposed metric not only has outstanding performance on JPEG and JPEG2000 images, but also applicable to other types of artifacts.

Adaptive DCT coding using the new universal image quality index

2005 ◽  
Author(s):  
Aldo Morales ◽  
Sedig Agili ◽  
Lakshmi P. Baskaran
Keyword(s):  
Image Quality ◽  
Quality Index ◽  
Image Quality Index ◽  
Dct Coding

No-training, no-reference image quality index using perceptual features

2013 ◽  
Vol 52 (5) ◽  
pp. 057003 ◽  
Author(s):  
Chaofeng Li ◽  
Yiwen Ju ◽  
Alan C. Bovik ◽  
Xiaojun Wu ◽  
Qingbing Sang
Keyword(s):  
Image Quality ◽  
Quality Index ◽  
Reference Image ◽  
Perceptual Features ◽  
Image Quality Index

3D image quality index using SDP-based binocular perception model

IVMSP 2013 ◽  
2013 ◽  
Author(s):  
Hyunsuk Ko ◽  
Chang-Su Kim ◽  
Seo Young Choi ◽  
C.-C. Jay Kuo
Keyword(s):  
Image Quality ◽  
Quality Index ◽  
3D Image ◽  
Perception Model ◽  
Image Quality Index ◽  
Binocular Perception

scholarly journals Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance

2021 ◽  
Vol 11 (10) ◽  
pp. 4661
Author(s):  
Aladine Chetouani ◽  
Marius Pedersen
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Feature Vector ◽  
Pearson Correlation ◽  
Image Database ◽  
Viewing Distance ◽  
Quality Metric ◽  
Image Quality Metric ◽  
Fully Connected

An abundance of objective image quality metrics have been introduced in the literature. One important essential aspect that perceived image quality is dependent on is the viewing distance from the observer to the image. We introduce in this study a novel image quality metric able to estimate the quality of a given image without reference for different viewing distances between the image and the observer. We first select relevant patches from the image using saliency information. For each patch, a feature vector is extracted from a convolutional neural network model and concatenated at the viewing distance, for which the quality is predicted. The resulting vector is fed to fully connected layers to predict subjective scores for the considered viewing distance. The proposed method was evaluated using the Colourlab Image Database: Image Quality and Viewing Distance-changed Image Database. Both databases provide subjective scores at two different viewing distances. In the Colourlab Image Database: Image Quality we obtain a Pearson correlation of 0.87 at both 50 cm and 100 cm viewing distances, while in the Viewing Distance-changed Image Database we obtained a Pearson correlation of 0.93 and 0.94 at viewing distance of four and six times the image height. The results show the efficiency of our method and its generalization ability.

Validation of no-reference image quality index for the assessment of digital mammographic images

2016 ◽  
Author(s):  
Helder C. R. de Oliveira ◽  
Bruno Barufaldi ◽  
Lucas R. Borges ◽  
Salvador Gabarda ◽  
Predrag R. Bakic ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Index ◽  
Reference Image ◽  
Mammographic Images ◽  
Image Quality Index
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