Peak signal-to-noise ratio revisited: Is simple beautiful?

Korhonen, Jari; You, Junyong

doi:10.1109/qomex.2012.6263880

Cited by 144 publications

(73 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The closer the SSIM is to 1.0 the higher the quality image we have [46,48]. Another evaluation metric is peak signal to noise ratio (PSNR) or signal to noise ratio (SNR) [49,50] which is commonly used in the signal processing area as an image quality metric. PSNR is expressed as [51]:…”

Section: Quantitative Results: Similarity Metricsmentioning

confidence: 99%

AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

Rahnemoonfar

Johnson

Paden

2019

Sensors

View full text Add to dashboard Cite

Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.

show abstract

Section: Quantitative Results: Similarity Metricsmentioning

confidence: 99%

AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

Rahnemoonfar

Johnson

Paden

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…While not considered as QoE metric, PSNR (Peak Signal Noise Ratio) enables a quality ranking of the same video content subject to different impairments [43,27]. However, it does not necessarily correlate well with human-perception in general settings.…”

Section: Approachmentioning

confidence: 99%

A QoE Perspective on Sizing Network Buffers

Hohlfeld

Pujol

Ciucu

et al. 2014

Proceedings of the 2014 Conference on Internet Measurement Conference

View full text Add to dashboard Cite

Despite decades of operational experience and focused research efforts, standards for sizing and configuring buffers in network systems remain controversial. An extreme example of this is the recent claim that excessive buffering (i.e., bufferbloat) can severely impact Internet services. In this paper, we systematically examine the implications of buffer sizing choices from the perspective of factors impacting end user experience. To assess user perception of application quality under various buffer sizing schemes we employ Quality of Experience (QoE) metrics. We evaluate these metrics over a wide range of end-user applications (e.g., web browsing, VoIP, and RTP video streaming) and workloads in two realistic testbeds emulating access and backbone networks. The main finding of our extensive evaluations is that network workload, rather than buffer size, is the primary determinant of end user QoE. Our results also highlight the relatively narrow conditions under which bufferbloat seriously degrades QoE, i.e., when buffers are oversized and sustainably filled.

show abstract

“…Watermark imperceptibility expresses similarity between original and watermarked image [8]. To evaluate the perceptual quality of watermarked image Peak-Signal-to-Noise-Ratio (PSNR) can be used [9]. It is widely used metric in image processing, what uses mean square error calculation to assess objectively visual quality [10].…”

Section: Introductionmentioning

confidence: 99%

A Robust Color Image Watermarking Scheme Using Entropy and QR Decomposition

Laur¹,

Rasti²,

Agoyi³

et al. 2015

RADIOENGINEERING

View full text Add to dashboard Cite

Internet has affected our everyday life drastically. Expansive volumes of information are exchanged over the Internet consistently which causes numerous security concerns. Issues like content identification, document and image security, audience measurement, ownership, copyrights and others can be settled by using digital watermarking. In this work, robust and imperceptible nonblind color image watermarking algorithm is proposed, which benefit from the fact that watermark can be hidden in different color channel which results into further robustness of the proposed technique to attacks. Given method uses some algorithms such as entropy, discrete wavelet transform, Chirp z-transform, orthogonal-triangular decomposition and Singular value decomposition in order to embed the watermark in a color image. Many experiments are performed using well-known signal processing attacks such as histogram equalization, adding noise and compression. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks.

show abstract

Peak signal-to-noise ratio revisited: Is simple beautiful?

Cited by 144 publications

References 4 publications

AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

A QoE Perspective on Sizing Network Buffers

A Robust Color Image Watermarking Scheme Using Entropy and QR Decomposition

Contact Info

Product

Resources

About