A nonlocal HEVC in-loop filter using CNN-based compression noise estimation

Visual noise is often regarded as a disturbance in image quality, whereas it can also provide a crucial clue for image-based forensic tasks. Conventionally, noise is assumed to comprise an additive Gaussian model to be estimated and then used to reveal anomalies. However, for real sensor noise, it should be modeled as signal-dependent noise (SDN). In this work, we apply SDN to splicing forgery localization tasks. Through statistical analysis of the SDN model, we assume that noise can be modeled as a Gaussian approximation for a certain brightness and propose a likelihood model for a noise level function. By building a maximum a posterior Markov random field (MAP-MRF) framework, we exploit the likelihood of noise to reveal the alien region of spliced objects, with a probability combination refinement strategy. To ensure a completely blind detection, an iterative alternating method is adopted to estimate the MRF parameters. Experimental results demonstrate that our method is effective and provides a comparative localization performance.

Statistical Analysis of Signal-Dependent Noise: Application in Blind Localization of Image Splicing Forgery

Noise is the most common type of image distortion affecting human visual perception. In this paper, we propose a no-reference image quality assessment (IQA) method for noisy images incorporating the features of entropy, gradient, and kurtosis. Specifically, image noise estimation is conducted in the discrete cosine transform domain based on skewness invariance. In the principal component analysis domain, kurtosis feature is obtained by statistically counting the significant differences between images with and without noise. In addition, both the consistency between the entropy and kurtosis features and the subjective scores are improved by combining them with the gradient coefficient. Support vector regression is applied to map all extracted features into an integrated scoring system. The proposed method is evaluated in three mainstream databases (i.e., LIVE, TID2013, and CSIQ), and the results demonstrate the superiority of the proposed method according to the Pearson linear correlation coefficient which is the most significant indicator in IQA.

No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis

Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types. The first contribution of our research was to design a noise data feature extractor that can effectively extract noise information from the image pair. The second contribution of our work leveraged other noise parameter estimation algorithms that can only predict one type of noise. Our proposed method, DE-G, can estimate additive noise, multiplicative noise, and impulsive noise from single-source images accurately. We also show the capability of the proposed method in estimating multiple corruptions.

/pdf/a-study-on-distortion-estimation-based-on-image-gradients-j6bk30bz.pdf

A Study on Distortion Estimation Based on Image Gradients

The precise estimation of the noise level is a crucial issue in image processing. In this study, the authors propose a new method for noise standard deviation (STD) estimation from natural images based on skewness-scale invariance in the transform domain and an adaptive noise injection strategy. The method is divided into two steps. The first step assumes that the natural clean image has the property of constancy of skewness in the transform domain. Then, a preliminary noise estimation method based on skewness invariance is designed by solving a constrained non-linear optimisation problem. The second step involves noise rectification via noise injection. According to the phenomenon that compared with the high-noise circumstance, the error of preliminary estimation is more serious under a low amount of noise, the noise STD is re-estimated by injecting another noise for which the STD is known. In addition, the threshold model with respect to image complexity is established to identify whether a second estimation is needed. The experimental results demonstrate the efficacy of the proposed method and performance is superior to other state-of-the-art methods.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2019.1548

Efficient image noise estimation based on skewness invariance and adaptive noise injection

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Efficient image noise estimation based on skewness invariance and adaptive noise injection