ON BETWEEN-COEFFICIENT CONTRAST MASKING OF DCT BASIS FUNCTIONS

By Nikolay N. Ponomarenko, F. Silvestri, Karen O. Egiazarian, Marco Carli, Jaakko T. Astola, Vladimir Vasilyevich Lukin. 2007

454 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1a31c0a0ce47df40f5f7a610ef5406982aa376ac

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Zitong Yu
2. Nikolay N. Ponomarenko
3. A. Bovik
4. Kede Ma
5. Zhou Wang
6. Ke Gu
7. Wei Peng
8. C.-C. Jay Kuo

Citations in the corpus

• Image Quality Assessment by Comparing CNN Features between Images (2016, background): “[40] introduced an image quality metric based on PSNR and local contrast.”
• Variable Rate Image Compression with Recurrent Neural Networks (2015, unknown): “Note that while there are other metrics (e.g., Ponomarenko et al., 2007) which emulate the human visual system better than SSIM, we chose to use SSIM here due to its ubiquity and ease of comparison with previous work.”
• Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement (2019, methodology): “In future, we will try using compression related metrics like PSNR-HVS-M [20] to constrain the enhancement model STVEN.”
• Image database TID 2013 : Peculiarities , results and perspectives (2016, methodology): “Correspondence to HVS has been evaluated for the following metrics (quality indices): SFF [44], componentwise FSIM and its color version FSIMc [20], PSNR-HA and PSNR-HMA [43], SR-SIM [45], MSSIM [46], MAD index [27], IW-SSIM [19], MSDDM [47], IW-PSNR [19], color version of PSNR which takes into acco”
• Information Content Weighting for Perceptual Image Quality Assessment (2011, background): “• SSIM [5], MS-SSIM [42], visual signal-to-noise ratio (VSNR) [44], VIF [34], PSNR-HVS-M [45], and most apparent distortion (MAD) [17], which are state-of-the-art algorithms that have demonstrated competitive performance .”
• Image Quality Assessment Based on Gradient Similarity (2012, unknown): “In PSNR-HVS-M [5], MSE/PSNR in the discrete cosine transform domain is modified so that errors are weighted by the corresponding visibility threshold (which accounts for the masking effects of the HVS).”