DCTune: A TECHNIQUE FOR VISUAL OPTIMIZATION OF DCT QUANTIZATION MATRICES FOR INDIVIDUAL IMAGES.

By Andrew B. Watson. 1993

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Citations in the corpus

End-to-end optimization of nonlinear transform codes for perceptual quality (2016, background): “, [13], [14]), but these were generally limited to optimizing weighting matrices for the DCT.”
Spatio-Temporal Just Noticeable Distortion Profile for Grey Scale Image/Video in DCT Domain (2009, methodology): “More recently, Zhang [6] proposed an improved model based on the DCTune model, where more realistic luminance adaptation was considered and block classification was combined with the contrast masking to achieve better performance.”
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”
An Anti-Phishing System Employing Diffused Information (2014, methodology): “To accommodate such correlations, transform-domain methods have been proposed [Watson 1993].”
Statistical evaluation of image quality measures (2002, methodology): “The last quality measure we used that reflects the properties of the human visual system was the DCTune algorithm [56].”
Image information and visual quality (2006, unknown): “Some representative methods include [7]–[14].”
Perceptual image quality assessment using a normalized Laplacian pyramid (2016, background): “We define the quality of a distorted image, relative to its undistorted original, as the root mean squared error in this “normalized Laplacian” domain.”
TID2008 – A database for evaluation of full-reference visual quality assessment metrics (2004, methodology): “We have evaluated correspondence of HVS to the following 18 metrics (quality indices): MSSIM [8, 30], VIF [31, 30], a pixel based version of VIF (VIFP) [31, 30], VSNR [9, 30], PSNR-HVS (PSNRHVS) [28], PSNR-HVS-M (PSNRHVSM) [7], SSIM [6], NQM [32, 30], UQI [33], XYZ [34], LINLAB [35], IFC [36, 30], W”
41 OBJECTIVE VIDEO QUALITY ASSESSMENT (2003, methodology): “Watson’s DCT metric [40] is based on an 8×8 DCT transform commonly used in image and video compression.”
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms (2006, unknown): “DCTune [20].”
Information Content Weighting for Perceptual Image Quality Assessment (2011, methodology): “domain methods such as the mean squared error (MSE) and the structural similarity (SSIM) index [4], [5] compute pixelor patch-wise distortion/quality measures in space, while block-discrete cosine transform [6] and wavelet-based [7]–[11] approaches define localized quality/distortion measures across”
DCT-domain watermarking techniques for still images: detector performance analysis and a new structure (2000, methodology): “[18], [19], which has been applied by Watson to the adaptive computation of quantization matrices in the JPEG algorithm [20].”