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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.

We address the problem of efficient content-adaptive superpixel segmentation. Instead of adapting the size and/or amount of superpixels to the image content, we propose a warpingtransform that makes the image content more suitable to be segmented into regular superpixels. Regular superpixels in the warped image induce content-adaptive superpixels in the original image with improved segmentation accuracy. To efficiently compute the warping transform, we develop an iterative coarse-to-fine optimization procedure and employ a parallelization strategy allowing for a speedy GPU-based implementation. This solution works as a simple ‘add-on’ framework over an underlying segmentation algorithm and requires no additional parameters. Compared to the state-of-the-art methods, our approach provides competitive quality results and achieves a better time-accuracy trade-off. We further demonstrate the effectiveness of our method with an application to disparity estimation.

Objects at different spatial positions in an image exhibit different scales. Adaptive receptive fields are expected to capture suitable ranges of context for accurate pixel level semantic prediction. Recently, atrous convolution with different dilation rates has been used to generate features of multi-scales through several branches which are then fused for prediction. However, there is a lack of explicit interaction among the branches of different scales to adaptively make full use of the contexts. In this paper, we propose a Content-Adaptive Scale Interaction Network (CASINet) to exploit the multi-scale features for scene parsing. We build CASINet based on the classic Atrous Spatial Pyramid Pooling (ASPP) module, followed by a proposed contextual scale interaction (CSI) module, and a scale adaptation (SA) module. Specifically, in the CSI module, for each spatial position of some scale, instead of being limited by a fixed set of convolutional filters that are shared across different spatial positions for feature learning, we promote the adaptivity of the convolutional filters to spatial positions. We achieve this by the context interaction among the features of different scales. The SA module explicitly and softly selects the suitable scale for each spatial position and each channel. Ablation studies demonstrate the effectiveness of the proposed modules. We achieve state-of-the-art performance on three scene parsing benchmarks Cityscapes, ADE20K and LIP.

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.

Source camera identification is still a hard task in forensics community, especially for the case of the query images with small size. In this paper, we propose a solution to identify the source camera of the small-size images: content-adaptive fusion residual networks. According to the differences of the image contents, firstly, the images are divided into three subsets: saturation, smoothness and others. Then, we train three fusion residual networks for saturated images, smooth images, and others, separately, through transform learning. The fusion residual networks is formed with three paralleled residual networks and the difference of three residual networks lies in the convolutional kernel size of preprocessing layer. The features learned from the last residual blocks of three residual networks are fused and fed into softmax classifier. In particular, the residual networks is designed to learn better feature representation from the input data. The convolutional operation is added in preprocessing stage and three residual blocks are used. The experiment results show that the proposed method has satisfactory performances at three levels of source camera identification: brand level, model level, and device level.

Video codecs have several dozen parameters that subtly affect the encoding rate, quality and size of the compressed video. Codec developers, as a rule, provide standard presets that on average yield acceptable performance for all videos, but for a given video, certain parameters may yield more efficient encoding. In this paper, we propose a new approach to predicting video codec presets to increase compression efficiency. Our effort involved collecting a new representative video-sequence dataset from Vimeo.com. An experimental evaluation showed relative bitrate decreases of 17.8% and 7.9%, respectively for the x264 and x265 codecs with standard options, all while maintaining quality and speed. Comparison with other methods revealed significantly faster automatic preset selection with a comparable improvement in results. Finally, our proposed content-adaptive method predicts presets that archive better performance than codec-developer presets from MSU Codec Comparison 2020 [1].

This "success story" panel illustrates how next generation video applications are enabled on PCs today using consumer integrated and discrete GPUs launched in 2020. Intel's XeLP graphics technology with dedicated media hardware powers the integrated graphics in Intel's latest client processor, Tiger Lake, and Intel's first entry-level mainstream discrete graphics card, DG1. XeLP graphics in Tiger Lake and DG1 has democratized access to high performance implementations of the latest emerging video codec standards. Three topics will be covered: (i) 8K HEVC/AV1 Playback with Content Adaptive Power Optimization; (ii) Ludicrous Speed HEVC Encoding with Integrated + Discrete GPU; and (iii) MPEG Immersive Video (MIV) Playback on DG1.

With the advent of modern smartphones, AR, VR services and advancement in display resolution of mobile devices coupled with real-time streaming services, the demand for highresolution video has boomed. To fulfill this requirement, a variety of Adaptive Bit-Rate Streaming methods for Video-on-Demand applications are employed. However, the use of multi-pass encoding in the aforementioned methods renders them obsolete when it comes to real-time video streaming due to latency restrictions. In this work, we bypass the conventional multiple-encoding used in Video-on-Demand applications and present a novel machinelearning-based approach that estimates the optimal video resolution for a given content at a particular bit-rate for ultra low latency applications. A new feature that captures temporal as well as spatial correlation in video sequence has been used to train the Deep Neural Network (DNN) model. A python-based testbed is designed to evaluate the proposed scheme. Experiment results corroborate the viability and effectiveness of the proposed method for real-time mobile video streaming applications.

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.

Universal Steganalysis rely on extracting higher order statistical features that gets disturbed when hiding the message in a clean image. Due to content adaptive steganographies like HUGO, WOW etc. which embed the data more in textured areas of the image rather than smooth areas by minimizing the distortion of the image itself, first order features are not sufficient to differentiate clean and stego images. Thus, rich models come into picture in which a large number of features are extracted based on higher order noise residuals of clean and stego images. Thus, Universal Steganalyser is essentially a supervised classifier built on high dimensional feature set. To work with such high dimensional features on a large dataset of images is a very challenging task due to curse of dimensionality as well as computationally very expensive. This paper aims at comparing performance of three techniques-Ensemble classifier, Logistic regression and K-Nearest Neighbors on Spatial Rich Model features extracted for benchmarked dataset BOSSbase_1.01, for the better discrimination of clean and stego images.

This article introduces a novel RNN unit inspired by GRU, namely the Content-Adaptive Recurrent Unit (CARU). The design of CARU contains all the features of GRU but requires fewer training parameters. We make use of the concept of weights in our design to analyze the transition of hidden states. At the same time, we also describe how the content adaptive gate handles the received words and alleviates the long-term dependence problem. As a result, the unit can improve the accuracy of the experiments, and the results show that CARU not only has better performance than GRU, but also produces faster training. Moreover, the proposed unit is general and can be applied to all RNN related neural network models.

In this paper, we introduce a modification of the popular U-Net neural network architecture for medical image segmentation. Our proposed architecture replaces the concatenation operations in the traditional U-Net's skip connections with content-adaptive convolution, thereby significantly reducing the number of parameters of the network. Our experiments on two segmentation tasks - cell nuclei segmentation, and pneumo-thorax segmentation - demonstrated that the modified architecture achieves higher segmentation accuracy compared to the original U-net.

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.

Rate Control (RC) plays an important role in video encoding. Traditional solutions are using fixed rate or fixed quantization parameters as the unified rate-control targets for all videos in one given video application. However, unified ratecontrol targets tend to have some bad encoding cases because of applying wrong rate for the video content. In this paper, we propose one content-adaptive rate control solution. We employ one neural-network based model which can end-to-end learn the optimal rate-control target appropriate to the content characteristics. The experimental results show that the proposed model can predict the optimal rate-factor value with the accuracy up to 77.637%. With this model, the proposed video-encoding method can significantly decrease the encoding quality fluctuation.

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.

Aiming at the problem of choosing high-pass filters in image processing layer of deep CNN (Convolution Neural Network) for steganalysis, this paper studies the effect of different filter banks on detection performance for content-adaptive JPEG steganography, and proposes a steganalysis method based on multi-scale 2D Gabor filtering and ensemble of multiple deep CNNs. Firstly, the effect of high-pass filters of image processing layer on detection performance is studied and the detection errors corresponding to the different combinations of high-pass filters are shown, then the relevant experimental results are analyzed. Lastly, two typical content-adaptive JPEG steganography algorithms such as UED and J-UNIWARD are taken as examples, the proposed steganalysis method is compared with other methods to verify the effectiveness of the proposed method.

This paper proposes a framework of content-adaptive image compressed sensing using deep learning, which analyzes the image content and adaptively allocates samples for different image patches accordingly. Experimental results demonstrate that the proposed framework outperforms the state-of-the-arts both in subjective and objective quality, especially at low sampling rates. For example, when the sampling rate is 0.1, 1–6 dB improvement in peak signal to noise ratio (PSNR) is observed. Moreover, the proposed work reconstructs images with more details and less image blocking effects, leading to apparent visual improvement.

Aiming at improved rate-distortion (R-D) performance, this paper presents a machine-learning based solution for the run-time video resolution adaptation problem. The proposed approachutilizes neural networks that leverage a complexity feature extracted from the video frames topredict a quantization parameter (QP) for downscaled video targeting the same bitrate as the native video. The peak signal to noise ratio (PSNR) is also predicted for both the native and downscaled resolutions, and the one that leads to the highest PSNR is selected. Experimental results show that \quad the proposed adaptive approach achieves significant improvements in R-D performance compared to using a fixed resolution.

In this paper, a novel hybrid cubemap projection (HCP) is proposed to improve the 360-degree video coding efficiency. HCP allows adaptive sampling adjustments in the horizontal and vertical directions within each cube face. HCP parameters of each cube face can be adjusted based on the input 360-degree video content characteristics for a better sampling efficiency. The HCP parameters can be updated periodically to adapt to temporal content variation. An efficient HCP parameter estimation algorithm is proposed to reduce the computational complexity of parameter estimation. Experimental results demonstrate that HCP format achieves on average luma (Y) BD-rate reduction of 11.51%, 8.0%, and 0.54% compared to equirectangular projection format, cubemap projection format, and adjusted cubemap projection format, respectively, in terms of end-to-end WS-PSNR.

Detail enhancement is required by many problems in the fields of image processing and computational photography. Existing detail enhancement algorithms first decompose a source image into a base layer and a detail layer via an edge-preserving smoothing algorithm, and then amplify the detail layer to produce a detail-enhanced image. In this letter, we propose a new L 0 norm based detail enhancement algorithm which generates the detail-enhanced image directly. The proposed algorithm preserves sharp edges better than an existing L 0 norm based algorithm. Experimental results show that the proposed algorithm reduces color distortion in the detail-enhanced image, especially around sharp edges.

In high-efficiency coding (HEVC), the random-access structure (RAS) is employed due to its high coding efficiency and “random-access” performance. Pictures in RAS are assigned to different temporal layers. And how to select the QP for each temporal layer in quantization parameter cascading (QPC) technique is critical for improving the coding efficiency of RAS. In order to further improve the coding efficiency of RAS, a QPC technique considering the video content characteristics (denoted as VC-QPC for short) is proposed. In VC-QPC, motion and texture complexities of a video are used for predicting the optimal quantization parameter values of temporal layers. Compared with the method in the test model of HEVC, i.e., HM14.0, the BD-rate of proposed VC-QPC is −5.60% for RAS.

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 proposes a content-adaptive sharpening algorithm using two-dimensional (2D) FIR filters trained by pre-emphasis for various image pairs. In the learning stage, all low-quality (LQ) and high-quality (HQ) image pairs are first pre-emphasized, i.e., properly sharpened. Then selective 2D FIR filter coefficients for high-frequency synthesis are trained using the pre-emphasized LQ–HQ image pairs, and then are stored in a dictionary that resembles an LUT (look-up table). In the inference stage, each input image is pre-emphasized in the same manner as in the learning stage. The best-matched 2D filter for each LQ patch is then found in the dictionary, and an HQ patch corresponding to the input LQ patch is synthesized using the resultant 2D FIR filter. The experiment results show that the proposed algorithm visually outperforms existing ones and that the mean of absolute errors (MAEs) and MSSSIM (multi-scale structure similarity) of the proposed algorithm are about 10% to 60% lower and about 0.002–0.053 higher, respectively than those of the existing algorithms.

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.

Near-eye multi-focal (light field) displays are known to be more advantageous than conventional stereoscopic 3D displays, because they can alleviate the vergence-accommodation conflict. While stereoscopic 3D displays are restricted to projecting images on a single focal plane, near-eye light field displays can form multiple focal planes and volumetrically render 3D data. Most existing near-eye light field displays use simplistic, uniformly spaced focal plane configuration (in dioptric space). In this paper, we present a novel technique that optimizes the focal plane configuration based on characteristics of the content to be rendered. We show that this technique can significantly improve the perceived visual quality of content visualized on such displays.

In this paper, a content-adaptive deblocking method is proposed to improve the visual quality of block-based DCT compressed videos. We find that the edge information obtained through the global orientation energy edge detection (OEED) on an initially deblocked image provides a robust partition of local directional features (LDFs). Based on this partition, for the directional featured region, we design corresponding filter orientation and thresholds to best preserve image details while reducing blocking artifacts; for the consecutive non-featured region, we impose extra smoothing to suppress the visually severe blocking artifacts. Experimental results demonstrate that our method effectively improves the visual quality while well maintaining the objective fidelity of block-based DCT compressed videos, compared with the H.264/AVC deblocking method and other existing directional deblocking methods.

We optimize automultiscopic displays built by stacking a pair of modified LCD panels. To date, such dual-stacked LCDs have used heuristic parallax barriers for view-dependent imagery: the front LCD shows a fixed array of slits or pinholes, independent of the multi-view content. While prior works adapt the spacing between slits or pinholes, depending on viewer position, we show both layers can also be adapted to the multi-view content, increasing brightness and refresh rate. Unlike conventional barriers, both masks are allowed to exhibit non-binary opacities. It is shown that any 4D light field emitted by a dual-stacked LCD is the tensor product of two 2D masks. Thus, any pair of 1D masks only achieves a rank-1 approximation of a 2D light field. Temporal multiplexing of masks is shown to achieve higher-rank approximations. Non-negative matrix factorization (NMF) minimizes the weighted Euclidean distance between a target light field and that emitted by the display. Simulations and experiments characterize the resulting content-adaptive parallax barriers for low-rank light field approximation.

Portable and mobile multimedia production faces special challenges posed by transmission bandwidth limitations. This paper describes experimental studies of the multimodal quality of compressed television materials for mobile and portable devices, discusses required optimizations and proposes a mathematical model for content adaptive bit rate allocation. Study results are used to discuss the characteristics of multimodal quality and support the proposition that, if multimodal audiovisual perceptual quality is taken into account, it will improve the quality of new portable and mobile multimedia applications.

In this paper, a method for Lagrange multiplier selection is proposed in the context of rate-distortion optimisation for wavelet-based scalable video coding targeting quality scalability. Despite the prevalence of the conventional method for Lagrange multiplier selection in hybrid video coding, the underlying formulation is not applicable to wavelet-based scalable video coding. To address the inherent challenges, a thorough analysis of the rate-distortion models for transform video coding is provided with regard to low and middle-to-high bit-rates, respectively. Based on the analysis, the models are consolidated according to experimental observations and the consolidated rate-distortion models serve as the basis for the derivation of the Lagrange multiplier. Considering the influence of the open-loop prediction structure on the rate-distortion performance, the Lagrange multiplier is initially derived for a single-targeted bit-rate. Moreover, the method for Lagrange multiplier selection in scalable video coding aiming at multiple-targeted bit-rates is proposed in a general sense of bit-rate range, varying from low to high bit-rates, building on the initially derived Lagrange multiplier for a single-targeted bit-rate. The proposed Lagrange multiplier is content adaptive and well suited for wavelet-based scalable video coding where quantisation steps are unavailable. Detailed performance evaluation of the proposed method for wavelet-based scalable video coding is provided with regard to a given targeted bit-rate and multiple-targeted bit-rates, respectively. The experimental results have demonstrated the effectiveness of the proposed Lagrange multiplier for rate-distortion optimisation considering quality scalability in wavelet-based scalable video coding.

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.

Cinematic virtual reality provides an immersive visual experience by presenting omnidirectional videos of real-world scenes. A key challenge is to develop efficient representations of omnidirectional videos in order to maximize coding efficiency under resource constraints, specifically, number of samples and bitrate. We formulate the choice of representation as a multi-dimensional, multiple-choice knapsack problem and show that the resulting representations adapt well to varying content. We also show that separation of the sampling and bit allocation constraints leads to a computationally efficient solution using Lagrangian optimization with only minor performance loss. Results across images and videos show significant coding gains over standard representations.

Source camera identification (SCI) is an important topic in image forensics. One of the most effective fingerprints for linking an image to its source camera is the sensor pattern noise, which is estimated as the difference between the content and its denoised version. It is widely believed that the performance of the sensor-based SCI heavily relies on the denoising filter used. This study proposes a novel sensor-based SCI method using content adaptive guided image filter (CAGIF). Thanks to the low complexity nature of the CAGIF, the proposed method is much faster than the state-of-the-art methods, which is a big advantage considering the potential real-time application of SCI. Despite the advantage of speed, experimental results also show that the proposed method can achieve comparable or better performance than the state-of-the-art methods in terms of accuracy.

This paper employs the concept of the content-adaptive residual and presents a low-dimensional feature set for detecting the grayscale steganography in spatial domain. The testing image is first segmented into three kinds of areas, that is, the smooth, edge, and textural areas. Then, different pixel predictors are used to calculate the residuals responded to different areas. The yielded different co-occurrence matrices are finally collected as the steganalytic features. Experiments reported show that the proposed method is effective and yields good performances when detecting popular steganographic algorithms such as LSB matching, EA, and HUGO.

Remote engineering systems are valuable tools to give visual assistance and remote support e.g. in Structural Health Monitoring (SHM) or Non-destructive Testing (NDT). They allow discussing a second opinion with a remote expert and thus reducing the human factor during testing and monitoring. For an optimal impression of the situation, the second person requires both a camera view of the location and the screen view of the SHM/NDT system used. The Online Maintenance Assistance (OMA) system implements this two-view collaboration. Remote partners can see and actively control measuring equipment, while observing details of the location in the camera window. Due to varying screen signals and communication properties, an adaption for optimal visual quality of the camera and screen signal compression is required. The adaption is performed in a Machine Learning System (MLS), which uses an adapted k-nearest neighbors algorithm to adapt signal compression parameters to the measurement task and the communication channel. doi: 10.12783/SHM2015/148

This paper provides a motion-based content-adaptive depth map enhancement algorithm to enhance the quality of the depth map and reduce the artifacts in the synthesized views. The proposed algorithm extracts depth cues from the motion distribution at the specific scenario of camera movement to align the distribution of depth and motion. In real world scenarios, when the camera is panning in horizontal direction, the nearer distance between the object and the camera, the larger motion will be, and vice versa; therefore, we could interpret the depth from motion in this. Moreover, in the scenario of fixed camera, the depth cue from motion could be derived in the same approach, and the depth variation within one moving object shall be small. Hence, the depth values of moving object should not be rapidly changing. In addition, this paper also employs the bi-directional motion-compensated infinite impulse response low-pass filter to stabilize the consistency of depth maps over time. As a consequence, the algorithm so introduced not only aligns the depth map to depth cues from motion but also enhance stability and consistency of depth maps in the spatial-temporal domain. Experiment results via enhanced depth maps show that the synthesized results would be better in both objective and subjective measurement in comparison with the results using original depth maps and the state-of-the-art depth enhancement algorithms.

In this abstract, we present preliminary evaluation of a full 4D reconstruction method aimed at cardiac gated emission tomography. The proposed method uses content adaptive mesh model (CAMM) to represent the 3D volumetric data. The nodes of the CAMM are then moved from a gate to gate, according to the motion obtained from a motion estimation algorithm, to form 4D deformable content adaptive mesh model (DCAMM).

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

Integrating efficient visual perceptual cues into standardized video coding framework can improve performance significantly. In this paper we propose to enhance AVS encoder by using the latest just noticeable distortion (JND) model to adjust DCT coefficients of prediction residues in a content adaptive way. To better modeling JND profile in AVS integer DCT domain, we further derive the JND mapping from the classical DCT domain to AVS Integer DCT domain. The experiment shows that the proposed algorithm can reduce the bitrate by about 13% on average, compared to the AVS standard encoder at similar visual quality.