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Recently, most background modeling (BM) methods claim to achieve real-time efficiency for low-resolution and standard-definition surveillance videos. With the increasing resolutions of surveillance cameras, full high-definition (full HD) surveillance videos have become the main trend and thus processing high-resolution videos becomes a novel issue in intelligent video surveillance. In this article, we propose a novel content-adaptive resizing framework (CARF) to boost the computation speed of BM methods in high-resolution surveillance videos. For each frame, we apply superpixels to separate the content of the frame to homogeneous and boundary sets. Two novel downsampling and upsampling layers based on the homogeneous and boundary sets are proposed. The front one downsamples high-resolution frames to low-resolution frames for obtaining efficient foreground segmentation results based on BM methods. The later one upsamples the low-resolution foreground segmentation results to the original resolution frames based on the superpixels. By simultaneously coupling both layers, experimental results show that the proposed method can achieve better quantitative and qualitative results compared with state-of-the-art methods. Moreover, the computation speed of the proposed method without GPU accelerations is also significantly faster than that of the state-of-the-art methods. The source code is available at https://github.com/nchucvml/CARF.

Digital imaging has significantly influenced the outcome of research in various disciplines. For example, artificial intelligence and robotics, biometric security, multimedia and image processing, etc. Technically, image processing and the Human Visual System (HVS) relies heavily on image enhancement to improve the content of the image. One of the biggest challenges in image processing is detail enhancement due to halo artefacts and gradient inversion artefacts at edges. It has been used to enhance the visual quality of an image. Most algorithms that used to enhance the detail of an image essentially depend on edge-preserving decomposition techniques. in general, the image consist of two major elements are a base layer and a detail layer, which extracted by edge-preserving decomposition algorithms. The detail layer is enhanced to improve the details of the generated image. we propose in this paper, a new model to preserve the sharp edges and achieve better visual quality than the existing norm-based algorithm to enhance the details of the image. Experiments show that the proposed method reduces the distortion at the edges. It improves the details of the generated image significantly.

In this paper, a content adaptive coding method for plenoptic video is proposed to reduce both of the spatial and temporal redundancy. Based on the spatial correlations among subapertures, the plenoptic video is divided into two subaperture groups: the central view videos and the residual videos. Coding methods, multiview coding method based on spatial correlation(S-MVC) and multiview coding method based on temporal correlation(T-MVC), are adopted for residual videos according to correlation and residual energy analysis, while the central view videos are always encoded by S-MVC. Correlation analysis is performed first, and residual energy analysis is further performed if necessary. Coding the central view videos and the residual videos separately reduces bitstream, choosing different encoding methods for residual videos makes full use of the characteristics of different video content. Both the central view videos and the residual videos are compressed by MV-HEVC. The experimental results demonstrate that the proposed method outperforms a pseudo-sequence-based MVC method for plenoptic video by an average of 25.06% bitrate saving.

Content-adaptive steganography intends to hide data in the complex texture content of the image. Recently, some secure steganography methods have been proposed to identify the textural complexity of an image. However, most of the techniques do not take into account the information of pixel variation around the central pixel in all possible directions and therefore they are unable to accurately analyse the texture complexity. This work offers a quality-enhanced and secure method of content-adaptive image steganography. The proposed method is divided into three sequential steps: image segmentation, pixel complexity identification, and data embedding. An input cover image is initially divided into small local regions and the pixel-complexity is identified based on the proposed Complex Block Prior (CBP) criterion. In a local block, a high pass filter (HPF) bank is applied and eight residual responses are obtained. Following the CBP criterion, a complexity level out of nine levels is assigned to an individualized pixel block. The pixels are then arranged in the priority of complexity from highest to lowest. Data embedding for the corresponding complexity level then takes place using the proposed adaptive embedding algorithm. Experimental results verify the preservation of visual quality of stego images produced by the proposed method. Three image datasets: Standard test images, BOWS2 and BOSS-base are used for the experimentation and comparison with prior state-of-art methods. Highest values of the IQ (image quality) parameters e.g., SSIM and WPSNR show the effectiveness of the proposed method.

This paper presents a novel fast transcoding framework to efficiently bridge the state-of-art high efficiency video coding (HEVC) standard and its screen content coding (SCC) extension to support the bitstream compatibility over legacy HEVC devices. By exploiting the side information from the SCC bitstream, fast mode and partition decisions are made to accurately translate the novel SCC modes to conventional HEVC modes based on statistical mode mapping techniques. Compared with the full-decoding-full-encoding (FDFE) solution, the proposed framework achieves on average 51% and 82% complexity reductions with 0.57% Bjontegaard-delta rate (BD-Rate) loss and 9.74% BD-Rate gain under all-intra (AI) and low-delay (LD) configurations, respectively. Compared with the direct transcoding reusing intra mode and inter motion, the proposed mode mapping framework introduces additional 23% and 6% complexity reductions for AI and LD encoding configurations with 0.43% BD-Rate loss and 1.10% BD-Rate saving, respectively. The proposed solution is extended to support the single-input-multiple-output screen content adaptive streaming at the edge clouds, where an SCC bitstream coded in high quality is transcoded into multiple HEVC bitstreams in reduced qualities. Our proposed solution achieves on average 49% and 76% complexity reductions with 0.78% BD-Rate loss and 7.40% BD-Rate gain under AI and LD configurations, respectively.

For current steganalysis of the image content-adaptive steganography, there are multiple problems to be improved, such as high difficulty and low accuracy, when detecting images with various contents and textures. For this problem, an improved steganalytic method is proposed in this paper based on the pre-classification and feature selection. First, using the features extracted based on the dependency analysis of image adjacent data, the images with various content and texture complexities are pre-classified as multiple clusters by the K-means algorithms. Then, the performance of existing various steganalytic features are analyzed for different clusters of images, and the optimal features for each cluster are selected for final classification. The experimental results show that the detection accuracy could be improved by the proposed method, and the rationality and availability are also verified. At the same time, the analysis and experimental results in this paper also show that the images with rich content and complex texture should be paid more attention both in steganography and in steganalysis.

In this paper, we firstly analyze the statistical distribution of simultaneous sparse coding errors (SSCE), which reflects the local correlation and non-local correlation characteristics of natural images. Based on the observation, we establish the optimal denoising problem which uses L1 norm to constrain the image prior. According to the close-form solution of the proposed problem, we find that the denoising limit is only determined by the patch number, patch size and the variances of different bands in SSCE. Then we exploit the relationships between the patch complexity, patch size, patch number and the denoising limit. The study shows that a content-adaptive strategy may be useful to obtain better denoising performance. We then design a content-adaptive method for image denoising in which the groups are adaptively determined according to the structural complexities of reference patches. Experimental results show that the proposed scheme achieves competitive performance with several state-of-the-art methods in both subjective and objective aspects.

Imprecision and noise in the time series data may result in series with similar overall behaviors being recognized as being dissimilar because of the accumulation of many small local differences in noisy observations. While smoothing techniques can be used for eliminating such noise, the degree of smoothing that needs to be performed may vary significantly at di erent parts of the given time series. In this paper, we propose a content-adaptive smoothing, CA-Smooth, technique to reduce the impact of non-informative details and noise in time series by means of a data-driven approach to smoothing. The proposed smoothing process treats different parts of the time series according to local information content. We show the impact of di erent adaptive smoothing criteria on a number of samples from different datasets, containing series with diverse characteristics.

Image Super resolution is used to upscale the low resolution Images. It is also known as image upscaling. This paper focuses on upscaling of compressed images with interpolation based Single Image Super Resolution technique. A content adaptive interpolation method of image upscaling has been proposed. This interpolation based scheme is useful for single image based Super Resolution methods. The presented method works on horizontal, vertical and diagonal directions of an image separately and it is adaptive to the local content of an image. This method relies only on a single image and uses the content of the original image only; therefore, the proposed method is more practical and realistic. The simulation results have been compared to other standard methods with the help of various performance matrices like PSNR, MSE, MSSIM etc. which indicates the preeminence of the proposed method.

This paper proposes a content adaptive fast CU size selection algorithm for HEVC intra-prediction using weighted support vector machines. The proposed algorithm demonstrates an average encoding time reduction of 52.38% with 1.19% average BDBR increase compared to HM16.1 reference encoder.

In video applications it is necessary to continuously measure the video quality perceived by the end-user. Thus it is desirable to know which parts of video frame, i.e. which contents, attract viewers’ attention. If this information is known, then it is possible to estimate perceived video quality in a meaningful way. However, automatic detection of viewers’ fixation points is time-consuming process and often is omitted in objective video quality assessment (VQA) metrics. Based on our previous work, in which we proposed Foveation-based content Adaptive Root Mean Squared Error (FARMSE) VQA metric, in this work we propose two new full-reference (FR) VQA metrics called Multi-Point FARMSE (MP-FARMSE), and Simple-FARMSE (S-FARMSE). Both new-proposed metrics are based on foveated-vision features of human visual system and spatio-temporal features of video signal. In MP-FARMSE, by using an engineering approach, we implemented the fact that viewer’s attention can be directed out of the center of the frame, thus covering use-cases when objects of interest are not located in the center of the frame. The main idea when creating the S-FARMSE metric was to reduce the computational complexity of the final algorithm and to make S-FARMSE metric capable of processing high-resolution video signals in real-time. Performances of the new-proposed metrics are compared to performances of seven existing VQA metrics on two different video quality databases. The results show that performances achieved by MP-FARMSE and S-FARMSE are quite close to those of state-of-the-art VQA metrics, whereas at the same time their computational complexity level is significantly lower.

When the video is compressed and transmitted over heterogeneous networks, it is necessary to ensure the satisfying quality for the end user. Since human observers are the end users of video applications, it is very important that the human visual system (HVS) characteristics are taken into account during the video quality evaluation. This paper deals with video quality assessment (VQA) based on HVS characteristics and proposes a novel full-reference (FR) VQA metric called the Foveation-based content Adaptive Root Mean Squared Error (FARMSE). FARMSE uses several HVS characteristics that significantly influence perception of distortions in a video. Primarily these are foveated vision, reduction of the spatial acuity due to motions as well as spatial masking. Foveated vision is related to variable resolution of HVS across the viewing field, where the highest resolution is at the point of fixation. The point of fixation is projected onto the fovea – the area of retina with the highest density of photoreceptors. The part of image that falls on fovea is perceived by the highest acuity, whereas the spatial acuity decreases as the distance of the image part from the fovea increases. Spatial acuity further decreases if eyes cannot track moving objects. Both mentioned mechanisms influence contrast sensitivity of the HVS. Contrast sensitivity is frequency dependent and FARMSE uses Haar filters to utilize this dependence. Furthermore, spatial masking is implemented in each frequency channel. The FARMSE performance is compared to this of nine state-of-the-art VQA metrics on two different databases, LIVE and ECVQ. Additionally, the metrics are compared in terms of calculation complexity. The performed experiments show that FARMSE achieves high performance when predicting the quality of videos with different resolutions, degradation types and content types. FARMSE results outperform the results of most of the analyzed metrics, whereas they are comparable to these of the best publicly available metrics, including the well-known MOtion-based Video Integrity Evaluation (MOVIE) index. Besides that, FARMSE calculation complexity is significantly lower than that of the metrics comparable thereto in terms of prediction accuracy.

This study proposes a subpixel-based image downsampling algorithm using content-adaptive two-dimensional (2D) finite impulse response (FIR) filters. The proposed algorithm consists of a learning stage and an inference stage. In the learning stage, using a sufficient number of low-resolution (LR) and high-resolution (HR) patch pairs, the authors compute optimal 2D FIR filters to synthesise LR patches of the highest quality from a specific HR patch and store the patch-adaptive 2D FIR filters in a dictionary. In the inference stage, they explore candidates that best match to each HR input patch in the dictionary and synthesise LR patches by using their corresponding 2D FIR filters on a subpixel basis. The experimental results show that the proposed algorithm produces higher-quality LR images on a patch basis than existing methods and entails no blur and aliasing artefacts.

This paper presents a steganalytic approach against video steganography which modifies motion vector (MV) in content adaptive manner. Current video steganalytic schemes extract features from fixed-length frames of the whole video and do not take advantage of the content diversity. Consequently, the effectiveness of the steganalytic feature is influenced by video content and the problem of cover source mismatch also affects the steganalytic performance. The goal of this paper is to propose a steganalytic method which can suppress the differences of statistical characteristics caused by video content. The given video is segmented to subsequences according to block’s motion in every frame. The steganalytic features extracted from each category of subsequences with close motion intensity are used to build one classifier. The final steganalytic result can be obtained by fusing the results of weighted classifiers. The experimental results have demonstrated that our method can effectively improve the performance of video steganalysis, especially for videos of low bitrate and low embedding ratio.

Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement. Although plenty of efforts have been dedicated to this task, several issues still remain unsolved for generating vivid and detail-preserving personal sketch portraits. For example, quite a few artifacts may exist in synthesizing hairpins and glasses, and textural details may be lost in the regions of hair or mustache. Moreover, the generalization ability of current systems is somewhat limited since they usually require elaborately collecting a dictionary of examples or carefully tuning features/components. In this paper, we present a novel representation learning framework that generates an end-to-end photo-sketch mapping through structure and texture decomposition. In the training stage, we first decompose the input face photo into different components according to their representational contents (i.e., structural and textural parts) by using a pre-trained Convolutional Neural Network (CNN). Then, we utilize a Branched Fully Convolutional Neural Network (BFCN) for learning structural and textural representations, respectively. In addition, we design a Sorted Matching Mean Square Error (SM-MSE) metric to measure texture patterns in the loss function. In the stage of sketch rendering, our approach automatically generates structural and textural representations for the input photo and produces the final result via a probabilistic fusion scheme. Extensive experiments on several challenging benchmarks suggest that our approach outperforms example-based synthesis algorithms in terms of both perceptual and objective metrics. In addition, the proposed method also has better generalization ability across dataset without additional training., Comment: Published in TIP 2017

The purpose of this exploratory was to examine the possible impact of the content adaptive selling, listening and customer orientation, based sales performance. Eighty eight sales people were surveyed by questionnaire. Result indicated that adaptive selling positively influence sales performance. Listening positively influence sales performance. Customer orientation positively influence sales performance. Listening is a stronger influence on sales performance. All result was using a hipotesis test. Keywords : adaptive selling, listening, customer orientation.

Skin detection plays an important role in applications such as face detection and tracking, person detection and pornography detection. While previous studies focus on pixel-based skin color detection techniques that individually classify each pixel as skin color or non-skin color, this study presents a region-based algorithm for detecting skin color. The proposed algorithm uses a special region, called key skin region, as the basis to classify skin color. A performance comparison with conventional skin classifiers, including the Bayesian classifier, the unimodal Gaussian classifier and the Gaussian mixture classifier, is made in this study. Experimental results show that the proposed algorithm outperforms other tested skin classifiers. Furthermore, the skin regions detected by the proposed algorithm, especially facial regions, are nearly complete with no hollow holes in these regions. This property can simplify the complexity of implementing applications that use skin color as their basis, such as face detection and face tracking.

A content adaptive, network aware approach for joint optimization of wireless video transmission is presented in this article. The joint system optimization approach has been developed in the framework of the IST PHOENIX project (www.ist-phoenix.org). After a description of the considered cross-layer approach and of the information to be exchanged among the system component blocks, the concept of "JSCC/D controllers" is introduced. Results obtained through the demonstrator realized as proof-of-concept for the described paradigm are then shown, confirming the validity of the described joint optimization approach

This paper proposes an efficient content adaptive screen image scaling scheme for the real-time screen applications like remote desktop and screen sharing. In the proposed screen scaling scheme, a screen content classification step is first introduced to classify the screen image into text and pictorial regions. Afterward, we propose an adaptive shift linear interpolation algorithm to predict the new pixel values with the shift offset adapted to the content type of each pixel. The shift offset for each screen content type is offline optimized by minimizing the theoretical interpolation error based on the training samples respectively. The proposed content adaptive screen image scaling scheme can achieve good visual quality and also keep the low complexity for real-time applications., ICIP 2013

Limited by long acquisition time of 2D ghost imaging, current ghost imaging systems are so far inapplicable for dynamic scenes. However, it's been demonstrated that nature images are spatiotemporally redundant and the redundancy is scene dependent. Inspired by that, we propose a content-adaptive computational ghost imaging approach to achieve high reconstruction quality under a small number of measurements, and thus achieve ghost imaging of dynamic scenes. To utilize content-adaptive inter-frame redundancy, we put the reconstruction under an iterative reweighted optimization, with non-uniform weight computed from temporal-correlated frame sequences. The proposed approach can achieve dynamic imaging at 16fps with 64×64-pixel resolution.

This paper presents a content-adaptive coding scheme for immersive networked experience of sports events, in particular, soccer games. We assume that future sports events are captured by an array of fixed high-definition cameras which provide multiview image sequences for a free-viewpoint immersive networked experience in a home environment. We discuss a content-adaptive coding scheme for image sequences that exploits properties of such sequences and that permits efficient user interactions. In this work, we construct a rate distortion model for an image sequence to obtain the optimal bitrate allocation among static and dynamic content items. The optimal bitrate allocation results in a rate distortion performance of the coding scheme that outperforms that of conventional H.264/AVC coding significantly.

Fourier ptychography (FP) is a recently reported technique, for large field-of-view and high-resolution imaging. Specifically, FP captures a set of low-resolution images, under angularly varying illuminations, and stitches them together in the Fourier domain. One of FP's main disadvantages is its long capturing process, due to the requisite large number of incident illumination angles. In this Letter, utilizing the sparsity of natural images in the Fourier domain, we propose a highly efficient method, termed adaptive Fourier ptychography (AFP), which applies content adaptive illumination for FP, to capture the most informative parts of the scene's spatial spectrum. We validate the effectiveness and efficiency of the reported framework, with both simulated and real experiments. Results show that the proposed AFP could shorten the acquisition time of conventional FP, by around 30%-60%.