Using visible SNR (vSNR) to compare the image quality of pixel binning and digital resizing

被引:8
|
作者
Farrell, Joyce [1 ]
Okincha, Mike [2 ]
Parmar, Manu [1 ,3 ]
Wandell, Brian [1 ,3 ]
机构
[1] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
[2] Omnivis Technol, Santa Clara, CA 95054 USA
[3] Stanford Univ, Dept Psychol, Stanford, CA 94305 USA
来源
DIGITAL PHOTOGRAPHY VI | 2010年 / 7537卷
关键词
sensor design; image quality; pixel binning; imaging pipeline;
D O I
10.1117/12.839149
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
We introduce a new metric, the visible signal-to-noise ratio (vSNR), to analyze how pixel-binning and resizing methods influence noise visibility in uniform areas of an image. The vSNR is the inverse of the standard deviation of the S-CIELAB representation of a uniform field; its units are 1/Delta E. The vSNR metric can be used in simulations to predict how imaging system components affect noise visibility. We use simulations to evaluate two image rendering methods: pixel binning and digital resizing. We show that vSNR increases with scene luminance, pixel size and viewing distance and decreases with read noise. Under low illumination conditions and for pixels with relatively high read noise, images generated with the binning method have less noise (high vSNR) than resized images. The binning method has noticeably lower spatial resolution. The binning method reduces demands on the ADC rate and channel throughput. When comparing binning and resizing, there is an image quality tradeoff between noise and blur. Depending on the application users may prefer one error over another.
引用
收藏
页数:9
相关论文
共 50 条
  • [41] Pixel-driven computation of parallel and fan-beam projections of a digital image based on pixel-representation using a new formula
    Galigekere, Ramesh R.
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2022, 216
  • [42] Enhancement of digital radiography image quality using a convolutional neural network
    Sun, Yuewen
    Li, Litao
    Cong, Peng
    Wang, Zhentao
    Guo, Xiaojing
    JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, 2017, 25 (06) : 857 - 868
  • [43] A concise review on food quality assessment using digital image processing
    Meenu, Maninder
    Kurade, Chinmay
    Neelapu, Bala Chakravarthy
    Kalra, Sahil
    Ramaswamy, Hosahalli S.
    Yu, Yong
    TRENDS IN FOOD SCIENCE & TECHNOLOGY, 2021, 118 : 106 - 124
  • [44] Image quality of digital subtraction angiography using flat detector technology
    Ducourant, T
    Couder, D
    Wirth, T
    Trochet, JC
    Bastians, R
    Bruijns, T
    Luijendijk, H
    Sandkamp, B
    Davies, A
    Didier, D
    Gonzalez, A
    Terraz, S
    Rüfenach, D
    MEDICAL IMAGING 2003: PHYSICS OF MEDICAL IMAGING, PTS 1 AND 2, 2003, 5030 : 203 - 214
  • [45] Digital radiography image quality evaluation using various phantoms and software
    Tsalafoutas, Ioannis A.
    AlKhazzam, Shady
    Tsapaki, Virginia
    AlNaemi, Huda
    Kharita, Mohammed Hassan
    JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, 2022, 23 (12):
  • [46] Reduced reference stereoscopic image quality assessment using digital watermarking
    Zhou, Wujie
    Jiang, Gangyi
    Yu, Mei
    Wang, Zhongpeng
    Peng, Zongju
    Shao, Feng
    COMPUTERS & ELECTRICAL ENGINEERING, 2014, 40 (08) : 104 - 116
  • [47] Investigations of strength and quality of clinched joints using digital image correlation
    Eshtayeh, Mohanna
    Hrairi, Meftah
    NONDESTRUCTIVE TESTING AND EVALUATION, 2024,
  • [48] Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation
    Hagiwara, A.
    Otsuka, Y.
    Hori, M.
    Tachibana, Y.
    Yokoyama, K.
    Fujita, S.
    Andica, C.
    Kamagata, K.
    Irie, R.
    Koshino, S.
    Maekawa, T.
    Chougar, L.
    Wada, A.
    Takemura, M. Y.
    Hattori, N.
    Aoki, S.
    AMERICAN JOURNAL OF NEURORADIOLOGY, 2019, 40 (02) : 224 - 230
  • [49] An evaluation and comparison of digital spot image quality obtained using large FOV image intensifiers
    Peter, MB
    Pavlicek, W
    Owen, JM
    RADIOLOGY, 1999, 213P : 236 - 236
  • [50] A Novel Metric for Digital Image Quality Assessment using Entropy-Based Image Complexity
    Khanzadi, Pouria
    Majidi, Babak
    Akhtarkavan, Ehsan
    2017 IEEE 4TH INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED ENGINEERING AND INNOVATION (KBEI), 2017, : 440 - 445