Color Image Quality Assessment Based on Image Quality Parameters Perceived by Human Vision System

2010 ◽  
Author(s):  
Zheng-Xiang Xie ◽  
Zhi-Fang Wang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Color Image ◽  
Vision System ◽  
Quality Parameters ◽  
Human Vision ◽  
Human Vision System

Modern Furniture Color Image Based on Eye Tracking

2012 ◽  
Vol 157-158 ◽  
pp. 410-414 ◽  
Author(s):  
Ji Feng Xu ◽  
Han Ning Zhang
Keyword(s):  
Eye Tracking ◽  
Eye Movement ◽  
Color Image ◽  
Vision System ◽  
Human Vision ◽  
Relative Importance ◽  
Human Vision System ◽  
Eye Tracker ◽  
Color Features ◽  
The Relationship

The relationship between modern furniture color image and eye tracking has been of interest to academics and practitioners for many years. We propose and develop a new view and method exploring these connections, utilizing data from a survey of 31 testees’ eye tracking observed value. Using Tobii X120 eye tracker to analyze eye movement to furniture samples in different hue and tones colors, we highlight the relative importance of the effect of furniture color on human vision system and show that the connections between furniture color features with color image.

scholarly journals A statistical reduced-reference method for color image quality assessment

2014 ◽  
Vol 74 (19) ◽  
pp. 8685-8701 ◽  
Author(s):  
Mounir Omari ◽  
Mohammed El Hassouni ◽  
Abdelkaher Ait Abdelouahad ◽  
Hocine Cherifi
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Color Image ◽  
Reference Method

scholarly journals Tinjauan Literatur Sistematik tentang Structural Similarity Index Measure untuk Deteksi Anomali Gambar

2021 ◽  
Vol 7 (2) ◽  
pp. 75
Author(s):  
Halim Bayuaji Sumarna ◽  
Ema Utami ◽  
Anggit Dwi Hartanto
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Human Visual System ◽  
Vision System ◽  
Similarity Index ◽  
Structural Similarity ◽  
Human Vision ◽  
Human Vision System ◽  
Structural Similarity Index ◽  
Index Measure

Image enhancement merupakan prosedur yang digunakan untuk memproses gambar sehingga dapat memperbaiki atau meningkatkan kualitas gambar agar selanjutnya dapat dianalis untuk tujuan tertentu. Ada banyak algoritma image enhancement yang dapat diterapkan pada suatu gambar, salah satunya dapat menggunakan algoritma structural similarity index measure (SSIM), algoritma ini berfungsi sebagai alat ukur dalam menilai kualitas gambar, bekerja dengan membandingkan fitur structural dari gambar, dan kualitas gambar dijelaskan oleh kesamaan structural. Selain untuk menilai kualitas suatu gambar, SSIM dapat menjadi metode dalam menganalisis perbedaan gambar, sehingga diketahui anomali dari perbandingan dua gambar berdasarkan data structural dari sebuah gambar. Tinjauan literature sistematis ini digunakan untuk menganalisis dan fokus pada algoritma SSIM dalam mengetahui anomaly 2 gambar yang terlihat mirip secara human visual system. Hasil sistematis review menunjukkan bahwa penggunaan algoritma SSIM dalam menilai kualitas gambar berkorelasi kuat dengan HVS (Human Vision System) dan dalam deteksi anomaly gambar menghasilkan akurasi yang berbeda, karena terpengaruh intensitas cahaya dan posisi kamera dalam mengambil gambar sebagai dataset.Kata Kunci— SSIM, anomaly, gambar, deteksiImage enhancement is a procedure used to process images so that they can correct or improve image quality so that they can then be analyzed for specific purposes. Many image enhancement algorithms can be applied to an image. one of the usable methods is the structural similarity index measure (SSIM) algorithm, this algorithm serves as a measuring tool in assessing image quality. It works by comparing the structural features of images, and the image quality is explained by structural similarity. In addition to assessing the quality of an image, SSIM can be a method of analyzing image differences. So, the anomalies are known from the comparison of two images based on the structural data from an image. This systematic literature review is used to analyze and focus on the SSIM algorithm in knowing anomaly 2 images that look similar to the human visual system. Systematic review results show that the use of the SSIM algorithm in assessing image quality is strongly correlated with HVS (Human Vision System). In anomaly detection of images produces different accuracy because it is affected by light intensity and camera position in taking pictures as a dataset.Keywords— SSIM, anomaly, gambar, deteksi

Sign in / Sign up

Export Citation Format

Share Document