Zongming Guo

Efficient Generalized Integer Transform for Reversible Watermarking

In this letter, an efficient integer transform based reversible watermarking is proposed. We first show that Tians difference expansion (DE) technique can be reformulated as an integer transform. Then, a generalized integer transform and a payload-dependent location map are constructed to extend the DE technique to the pixel blocks of arbitrary length. Meanwhile, the distortion can be controlled by preferentially selecting embeddable blocks that introduce less distortion. Finally, the superiority of the proposed method is experimental verified by comparing with other existing schemes.

Bit Allocation for Spatial Scalability Coding of H.264/SVC With Dependent Rate-Distortion Analysis

We propose a model-based spatial layer bit allocation algorithm for H.264/scalable video coding (SVC) in this paper. The challenge of this problem lies in the fact that the rate-distortion (R-D) behavior of an enhancement layer is dependent on its preceding layers because of inter-layer prediction. To solve it, we first focus on the case of two spatial layers, derive the distortion and rate models of the dependent layer analytically, and develop a low-complexity bit allocation algorithm. It is shown by experimental results that the proposed two-layer bit allocation algorithm can achieve the coding performance close to the optimal R-D performance based on the full search method. Then, we extend this result to multilayer bit allocation by performing the two-layer allocation scheme recursively. Finally, we compare the performance of group of pictures-based and frame-based spatial layer bit allocation schemes at a fixed temporal resolution. The superior performance of the proposed spatial layer bit allocation algorithm is demonstrated using Joint Scalable Video Model reference software algorithm and two prior H.264/SVC rate control algorithms as the benchmarks.

mDASH: A Markov Decision-Based Rate Adaptation Approach for Dynamic HTTP Streaming

Dynamic adaptive streaming over HTTP (DASH) has recently been widely deployed in the Internet. It, however, does not impose any adaptation logic for selecting the quality of video fragments requested by clients. In this paper, we propose a novel Markov decision-based rate adaptation scheme for DASH aiming to maximize the quality of user experience under time-varying channel conditions. To this end, our proposed method takes into account those key factors that make a critical impact on visual quality, including video playback quality, video rate switching frequency and amplitude, buffer overflow/underflow, and buffer occupancy. Besides, to reduce computational complexity, we propose a low-complexity sub-optimal greedy algorithm which is suitable for real-time video streaming. Our experiments in network test-bed and real-world Internet all demonstrate the good performance of the proposed method in both objective and subjective visual quality.

Image Super-Resolution Based on Structure-Modulated Sparse Representation

Sparse representation has recently attracted enormous interests in the field of image restoration. The conventional sparsity-based methods enforce sparse coding on small image patches with certain constraints. However, they neglected the characteristics of image structures both within the same scale and across the different scales for the image sparse representation. This drawback limits the modeling capability of sparsity-based super-resolution methods, especially for the recovery of the observed low-resolution images. In this paper, we propose a joint super-resolution framework of structure-modulated sparse representations to improve the performance of sparsity-based image super-resolution. The proposed algorithm formulates the constrained optimization problem for high-resolution image recovery. The multistep magnification scheme with the ridge regression is first used to exploit the multiscale redundancy for the initial estimation of the high-resolution image. Then, the gradient histogram preservation is incorporated as a regularization term in sparse modeling of the image super-resolution problem. Finally, the numerical solution is provided to solve the super-resolution problem of model parameter estimation and sparse representation. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed algorithm. Experimental results demonstrate that our proposed algorithm, which can recover more fine structures and details from an input low-resolution image, outperforms the state-of-the-art methods both subjectively and objectively in most cases.

Context-Aware Sparse Decomposition for Image Denoising and Super-Resolution

Image prior models based on sparse and redundant representations are attracting more and more attention in the field of image restoration. The conventional sparsity-based methods enforce sparsity prior on small image patches independently. Unfortunately, these works neglected the contextual information between sparse representations of neighboring image patches. It limits the modeling capability of sparsity-based image prior, especially when the major structural information of the source image is lost in the following serious degradation process. In this paper, we utilize the contextual information of local patches (denoted as context-aware sparsity prior) to enhance the performance of sparsity-based restoration method. In addition, a unified framework based on the Markov random fields model is proposed to tune the local prior into a global one to deal with arbitrary size images. An iterative numerical solution is presented to solve the joint problem of model parameters estimation and sparse recovery. Finally, the experimental results on image denoising and super-resolution demonstrate the effectiveness and robustness of the proposed context-aware method.

Visibility enhancement using an image filtering approach

The misty, foggy, or hazy weather conditions lead to image color distortion and reduce the resolution and the contrast of the observed object in outdoor scene acquisition. In order to detect and remove haze, this article proposes a novel effective algorithm for visibility enhancement from a single gray or color image. Since it can be considered that the haze mainly concentrates in one component of the multilayer image, the haze-free image is reconstructed through haze layer estimation based on the image filtering approach using both low-rank technique and the overlap averaging scheme. By using parallel analysis with Monte Carlo simulation from the coarse atmospheric veil by the median filter, the refined smooth haze layer is acquired with both less texture and retaining depth changes. With the dark channel prior, the normalized transmission coefficient is calculated to restore fogless image. Experimental results show that the proposed algorithm is a simpler and efficient method for clarity improvement and contrast enhancement from a single foggy image. Moreover, it can be comparable with the state-of-the-art methods, and even has better results than them.

Joint image denoising using adaptive principal component analysis and self-similarity

The non-local means (NLM) has attracted enormous interest in image denoising problem in recent years. In this paper, we propose an efficient joint denoising algorithm based on adaptive principal component analysis (PCA) and self-similarity that improves the predictability of pixel intensities in reconstructed images. The proposed algorithm consists of two successive steps without iteration: the low-rank approximation based on parallel analysis, and the collaborative filtering. First, for a pixel and its nearest neighbors, the training samples in a local search window are selected to form the similar patch group by the block matching method. Next, it is factorized by singular value decomposition (SVD), whose left and right orthogonal basis denote local and non-local image features, respectively. The adaptive PCA automatically chooses the local signal subspace dimensionality of the noisy similar patch group in the SVD domain by the refined parallel analysis with Monte Carlo simulation. Thus, image features can be well preserved after dimensionality reduction, and simultaneously the noise is almost eliminated. Then, after the inverse SVD transform, the denoised image is reconstructed from the aggregate filtered patches by the weighted average method. Finally, the collaborative Wiener filtering is used to further remove the noise. The experimental results validate its generality and effectiveness in a wide range of the noisy images. The proposed algorithm not only produces very promising denoising results that outperforms the state-of-the-art methods in most cases, but also adapts to a variety of noise levels.

Implementation of HEVC decoder on x86 processors with SIMD optimization

High Efficient Video Coding (HEVC) is the next generation video coding standard in progress. Based on the traditional hybrid coding framework, HEVC implements enhanced tools to improve compression efficiency at the cost of far more computational payload than the capacity of real-time video applications. In this paper, we focus on the software implementation of a real-time HEVC decoder over modern Intel x86 processors. First, we identify the most time-consuming modules of HM 4.0 decoder, represented by motion compensation, adaptive loopfilter, deblocking filter and integer transform. Then the single-execution-multiple-data (SIMD) methods are proposed to optimize the computational performance of these modules. Experimental results show that the optimized decoder is more than 4 times faster than the HM 4.0 decoder, with decoding speed of over 40 frames per second for 1920×1080 resolution videos on Intel i5-2400 processor.

Objective Quality Assessment of Screen Content Images by Uncertainty Weighting

In this paper, we propose a novel full-reference objective quality assessment metric for screen content images (SCIs) by structure features and uncertainty weighting (SFUW). The input SCI is first divided into textual and pictorial regions. The visual quality of textual regions is estimated based on perceptual structural similarity, where the gradient information is adopted as the structural feature. To predict the visual quality of pictorial regions in SCIs, we extract the structural features and luminance features for similarity computation between the reference and distorted pictorial patches. To obtain the final visual quality of SCI, we design an uncertainty weighting method by perceptual theories to fuse the visual quality of textual and pictorial regions effectively. Experimental results show that the proposed SFUW can obtain better performance of visual quality prediction for SCIs than other existing ones.

Deep Joint Rain Detection and Removal from a Single Image

In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain streak accumulation. Our core ideas lie in our new rain image model and new deep learning architecture. We add a binary map that provides rain streak locations to an existing model, which comprises a rain streak layer and a background layer. We create a model consisting of a component representing rain streak accumulation (where individual streaks cannot be seen, and thus visually similar to mist or fog), and another component representing various shapes and directions of overlapping rain streaks, which usually happen in heavy rain. Based on the model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output. The additional binary map is critically beneficial, since its loss function can provide additional strong information to the network. To handle rain streak accumulation (again, a phenomenon visually similar to mist or fog) and various shapes and directions of overlapping rain streaks, we propose a recurrent rain detection and removal network that removes rain streaks and clears up the rain accumulation iteratively and progressively. In each recurrence of our method, a new contextualized dilated network is developed to exploit regional contextual information and to produce better representations for rain detection. The evaluation on real images, particularly on heavy rain, shows the effectiveness of our models and architecture.