Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features 刘新 2016-03-21 Xue W, Mou X, Zhang L, et al. Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features[J]. IEEE Transactions on Image Processing, 2014, 23(11): 4850-4862. 被引用次数：23次

Introduction Why: Distortions introduced during image acquisition, compression, transmission, and storage, etc. For: evaluating practical systems, benchmarking image processing algorithms, designing imaging systems, and monitoring image acquisition and transmission.

Introduction Example: Comparing Image Enhancement Algorithms

Framework Methods: Framework: 1) Full Reference 2) Reduced Reference 3) No Reference Framework: 特征 测试图像 DMOS SVR 预测值 最终值 多种方法 比较评估 人工标注 已训练模型 非线性 映射 本文方法 SRC PCC RMSE

Features Local contrast features: Joint statistics: 1) the gradient magnitude (GM) map; 2) the Laplacian of Gaussian (LOG) response. Joint statistics: 1) The marginal distributions PG and PL; 2) The independency measures QG and QL.

Feature 1 The marginal distributions PG and PL: 1) is a local window centered at (i, j). 2) ω(l, k) are positive weights, we set ω(l, k) to be a spatially truncated Gaussian kernel rescaled to unit sum. 3) Km,n: the joint empirical probability function of G and L; the normalized bivariate histogram of G and L.

Feature 1 The GM and LOG maps as well as their marginal distributions before (middle column) and after (right column) joint adaptive normalization. (a) Houses; (b) Hats; and (c) Chessboard.

Feature 1 Marginal probabilities PG (shown as the first half of the histograms) and PL (shown as the second half of the histograms) of the distorted images generated from the same reference image at different DMOS levels.

Feature 2 The independency measures QG and QL: 1) We define Dm,n to measure the dependency between GM and LOG. 2) QG is the dependency of each specific value G = gm against all possible values of L. Using the marginal probability P(G = gm) as a weight, define the measure of the overall dependency of G = gm on L. Similarly, define the measure of the overall dependency of L = ln on G: QL. The proposed dependency measure can be viewed as the sum of conditional probabilities of a specific value of G (or L) over variable L (or G).

Feature 2 Above：Independency distribution between normalized GM and LOG features for images of (a) Houses, (b) Hats, and (c) Chessboard. Right：The independency distributions QG (shown as the first half of the histograms) and QL (shown as the second half of the histograms) of the distorted images of a reference image at different DMOS levels.

Result 1 Overall performance of the competing BIQA models on the three databases. The results of PSNR, SSIM and FSIM are also listed for reference: M1: only use the marginal distributions PG and PL to learn the quality prediction model; M2: only use the dependency measures QG and QL to learn; M3: use all statistical features PG, PL, QG and QL to learn the model.

Result 2 Performance (SRC) of competing BIQA models on individual distortion types:

Result 3 Performance (SRC) of the BIQA models across the three databases:

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