With the increasing demand for higher performance wireless local area networks (WLANs), channel bonding was first proposed in the IEEE 802.11n protocol to offer a higher data rate by combining two 20 MHz channels into one 40 MHz channel. Although much has been understood about channel bonding management, hardly any of these innovations have made it into today's IEEE 802.11 WLANs in a distributed manner. This paper presents the first step to fill the gap, by proposing a channel bonding management solution that can be readily implemented in today's commercial 802.11 devices. We conduct a measurement with off-the-shelf 802.11 devices in a real WLAN to characterize channel bonding, and then propose a channel bonding scheme based on adaptive channel clear assessment (CCA). We conduction evaluation under the typical IEEE 802.11 enterprise scenario, and the results show that our scheme improves the throughput by 37% and 46% compared to the traditional channel bonding scheme and the default CSMA/CA, respectively.

Dynamic video streaming protocols allow video clients to adaptively choose video rates to improve viewer Quality-of- Experience (QoE). Existing works on rate adaptation choose the video rate according to network capacity or buffer state, however, they didn't consider the early quitting problem. Most viewers quit before the video ends, in which case optimal solutions for complete video sessions may become suboptimal. In this paper, we design a video rate adaptation scheme which considers incomplete video sessions and aims at maximizing the quality of the video chunks that are actually watched by viewers. We formulate the video rate adaptation problem as a Markov Decision Process (MDP), in which the viewers' decision of quitting depends on the video quality and the rebuffer time. To solve the MDP problem, we propose two online learning algorithms for regular viewers and new viewers with no viewing history, respectively. The simulation results show that the proposed scheme increases the video quality {truly} consumed by the viewers, thus improving the viewer QoE.

Existing operational rate-distortion shape coding aims at finding a polygon which can be encoded with the lowest bit rate under a given upper bound on the edge error. However, this upper bound may cause noticeable errors. Therefore, we add an ℓ2-norm error regularization term to the objective function, and seek the globally optimal solution using a shortest path algorithm for a weighted directed acyclic graph. Experiments confirm the accuracy and robustness of our method.

Modeling subjective opinions on visual quality is a challenging problem, which closely relates to many factors of the human perception. In this paper, the additive log-logistic model (ALM) is proposed to formulate such a multidimensional nonlinear problem. The log-logistic model has flexible monotonic or nonmonotonic partial derivatives and thus is suitable to model various uni-type impairments. The proposed ALM metric adds the distortions due to each type of impairment in a log-logistic transformed space of subjective opinions. The features can be evaluated and selected by classic statistical inference, and the model parameters can be easily estimated. Cross validations on five Telecommunication Standardization Sector of International Telecommunication Union (ITU-T) subjectively-rated databases confirm that: 1) based on the same features, the ALM outperforms the support vector regression and the logistic model in quality prediction and, 2) the resultant no-reference quality met-ric based on impairment-relevant video parameters achieves high correlation with a total of 27 216 subjective opinions on 1134 video clips, even compared with existing full-reference quality metrics based on pixel differences. The ALM metric wins the model competition of the ITU-T Study Group 12 (where the validation databases are independent with the training databases) and thus is being put forth into ITU-T Recommendation P.1202.2 for the consent of ITU-T.

All existing video coding standards developed so far deem video as a sequence of natural frames (formed in the XY plane), and treat spatial redundancy (redundancy along X and Y directions) and temporal redundancy (redundancy along T direction) differently and separately. In this paper, we investigate into a new compression (redundancy reduction) method for video in which the frames are allowed to be formed in a non-XY plane. We are to exploit fuller extent of video redundancy, and propose an adaptive optimal compression plane determination process to be used as a preprocessing step prior to any standard video coding scheme. The essence of the scheme is to form the frames in the plane formed by two axes (among X, Y, and T) corresponding to signal correlation evaluation, which enables better prediction (therefore better compression). In spite of the simplicity of the proposed method, it can be used for both lossless and lossy compression, and with and without interframe prediction. Extensive experimental results show that the new coding method improves the performance of the video coding for a number of coding methods (inclusive of lossless and near-lossless Motion JPEG-LS, Motion JPEG, Motion JPG2K, H.264 intraonly profile, and H.264) and videos with different visual content.

Efficient image watermarking calls for full exploitation of the perceptual distortion constraint. Second-order statistics of visual stimuli are regarded as critical features for perception. This paper proposes a second-order statistics (SOS)-based image quality metric, which considers the texture masking effect and the contrast sensitivity in Karhunen-Loève transform domain. Compared with the state-of-the-art metrics, the quality prediction by SOS better correlates with several subjectively rated image databases, in which the images are impaired by the typical coding and watermarking artifacts. With the explicit metric definition, spread spectrum watermarking is posed as an optimization problem: we search for a watermark to minimize the distortion of the watermarked image and to maximize the correlation between the watermark pattern and the spread spectrum carrier. The simple metric guarantees the optimal watermark a closed-form solution and a fast implementation. The experiments show that the proposed watermarking scheme can take full advantage of the distortion constraint and improve the robustness in return.

In the research field of image processing, mean squared error (MSE) and peak signal-to-noise ratio (PSNR) are extensively adopted as the objective visual quality metrics, mainly because of their simplicity for calculation and optimization. However, it has been well recognized that these pixel-based difference measures correlate poorly with the human perception. Inspired by existing works, in this paper we propose a novel algorithm which separately evaluates detail losses and additive impairments for image quality assessment. The detail loss refers to the loss of useful visual information which affects the content visibility, and the additive impairment represents the redundant visual information whose appearance in the test image will distract viewer's attention from the useful contents causing unpleasant viewing experience. To separate detail losses and additive impairments, a wavelet-domain decoupling algorithm is developed which can be used for a host of distortion types. Two HVS characteristics, i.e., the contrast sensitivity function and the contrast masking effect, are taken into account to approximate the HVS sensitivities. We propose two simple quality measures to correlate detail losses and additive impairments with visual quality, respectively. Based on the findings inthat observers judge low-quality images in terms of the ability to interpret the content, the outputs of the two quality measures are adaptively combined to yield the overall quality index. By conducting experiments based on five subjectively-rated image databases, we demonstrate that the proposed metric has a better or similar performance in matching subjective ratings when compared with the state-of-the-art image quality metrics.

In this paper, a novel reduced-reference (RR) image quality assessment (IQA) is proposed by statistical modeling of the discrete cosine transform (DCT) coefficient distributions. In order to reduce the RR data rates and further exploit the identical nature of the coefficient distributions between adjacent DCT subbands, the DCT coefficients are reorganized into a three-level coefficient tree. Subsequently, generalized Gaussian density (GGD) is employed to model the coefficient distribution of each reorganized DCT subband. The city-block distance is employed to measure the difference between the two images. Experimental results demonstrate that only a small number of RR features is sufficient for representing the image perceptual quality. The proposed method outperforms the RR WNISM and even the full-reference (FR) quality metric PSNR.

Perceptual image coding requires an effective image quality metric, yet most of the existing metrics are complex and can hardly guide the compression effectively. This paper proposes a practical full-reference metric with consideration of the texture masking effect and contrast sensitivity function. The metric is capable of evaluating typical image impairments in real-world applications and can achieve the comparable performance as the state-of-the-art metrics on the publicly available subjectively-rated image databases. Due to its simplicity, the metric is embedded into JPEG image coding to ensure a better perceptual rate-distortion performance.

Contrast masking (CM) on edge and textured regions have to be distinguished since distortions on edge regions are easier to be noticed than that on textured regions. Therefore, how to efficiently estimate the CM on edge and textured regions of an image is a key issue for accurate JND (Just Noticeable Difference) estimation. An enhanced image domain JND estimator is devised in this paper with new model for CM. We use the total variation method to obtain a structural image (which contains edge information) and a textural image (which contains texture information) from the input image, and then evaluate the CM for the two images separately rather than the whole image, and hence edge and texture are better distinguished and the under-estimation of JND on textured regions can be effectively avoided. Experimental results of subjective viewing confirm that the proposed model is capable of determining more accurate visibility thresholds.

In this paper, we propose a full reference Video Quality Assessment (VQA) algorithm based on the Adaptive Block-size Transform Just-Noticeable Difference (ABT-JND) model. Firstly, ABT-JND is introduced for its efficiency of modeling the Human Vision System (HVS) characteristics. Based on the ABT-JND model, the full reference VQA is developed, by capturing HVS responses of spatio-temporal distortions over different block-size transforms. Experimental results have demonstrated that the proposed VQA outperforms other VQA methods, while slightly poorer than MOVIE. However, it maintains a very simple formulation. Since the proposed VQA performs on transform domain, it could be easily applied on many related applications, such as video compression, watermarking, and so on.

Visual quality assessment plays a crucial role in many vision-related signal processing applications. In the literature, more efforts have been spent on spatial visual quality measure. Although a large number of video quality metrics have been proposed, the methods to use temporal information for quality assessment are less diversified. In this paper, we propose a novel method to measure the temporal impairments. The proposed method can be incorporated into any image quality metric to extend it into a video quality metric. Moreover, it is easy to apply the proposed method in video coding system to incorporate with MSE for rate-distortion optimization.

Subjectively-rated image databases have become increasingly popular in the evaluation of image quality measurement algorithms. Several groups recently have improved their metrics' performance in matching these databases, using particular HVS (human visual system) properties or image statistical models. However, it is difficult to know whether these improvements are due to progress towards mimicking the perceptual properties, or are due to matching some characteristics of the databases. This paper demonstrates an inherent limitation in using such databases, showing that our very simple metric, built on the contrast masking effect, is able to perform as good as many state-of-the-art metrics. It is also argued that existent databases neither contain enough images with particularly biased distortions to test the significance of single HVS property, nor cover diverse distortion types to reflect the requirement of emerging applications.

In just noticeable difference (JND) models, evaluation of contrast masking (CM) is a crucial step. More specifically, CM due to edge masking (EM) and texture masking (TM) needs to be distinguished due to the entropy masking property of the human visual system. However, TM is not estimated accurately in the existing JND models since they fail to distinguish TM from EM. In this letter, we propose an enhanced pixel domain JND model with a new algorithm for CM estimation. In our model, total-variation based image decomposition is used to decompose an image into structural image (i.e., cartoon like, piecewise smooth regions with sharp edges) and textural image for estimation of EM and TM, respectively. Compared with the existing models, the proposed one shows its advantages brought by the better EM and TM estimation. It has been also applied to noise shaping and visual distortion gauge, and favorable results are demonstrated by experiments on different images.

In this paper, we investigate into a new lossless compression framework for video. We are to exploit fuller scale of temporal redundancy, and propose an adaptive optimal compression plane (OCP) determination process to be used with the standard intra-frame lossless predictive coding scheme. The essence of the scheme is to perform the prediction on the plane formed by two axes (among X, Y and T) corresponding to signal correlation evaluation, which enables better prediction (therefore better compression). In spite of the simplicity of the proposed method, simulation results show that the rate saving of the resultant system is 27.0% on average with respect to JPEG-LS, for a large number of videos with different visual content.

Decomposing an image into a texture part and a non-texture (cartoon) part, as well as grouping the texture part into several homogeneous subparts, is studied in this letter. The so-called texture part is composed of both the self-similar structure and the oscillatory noise. The self-similar structure of each homogenous subtexture is captured in its principal subspace. Both the segmentation and the decomposition are essentially related to sparse representation and are united to a framework.