High Perceptual Quality Research Articles

Optimal Bitstream Adaptation for Scalable Video Based On Two-Dimensional Rate and Quality Models

In this paper, a two-dimensional (2D) rate model is proposed considering the joint impact of spatial (i.e., the frame size) and SNR (i.e., the quantization step) resolutions on the overall rate-distortion performance. A related 2D quality model is then proposed in terms of perceptual quality. Then the two proposed models are applied to scalable video to address the problem of optimal bitstream adaptation. Experimental results show that the proposed rate and quality models fit the actual data very well, with high coefficients of determination and small relative root mean square errors. Moreover, given the bandwidth constraint and required display resolution of the end users, the optimal combination of SNR and spatial layers that provides the highest perceptual quality can be achieved using the proposed models.

Fuzzy Clustering Segmentation Algorithm Research for Biomedical Image Based on Artificial Life

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, the segmented partitions emerge when the state of the lives reaches an equilibrium. The artificial life approach is promising in image processing because it is inherently parallel and coincides with the self-governing biological process. Then combined with intensity-texture-position feature space in order to produce connected regions shown in the image, the final segmentation result is achieved at last. The experiment results prove that in the view of the biomedical image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

Dynamic Biomedical Image Segmentation Based on Wavelet Transform

In this paper, the image segmentation algorithm based on wavelet transform is presented. The proposed image segmentation algorithm performs the segmentation in the combined intensity-texture-position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. This segmentation algorithm is applied to reduced versions of the original images in order to speed-up the completion of the segmentation. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.

Content based Public Key Watermarking Scheme for Image Verification and Authentication

semi fragile, public key based digital signature scheme to verify and authenticate digital images is proposed. The scheme computes a content dependent feature value for each block of DCT coefficients of the original image. This feature is signed and then embedded into the selected DCT coefficients of the image as watermark. The technique aims to circumvent the cut and paste attacks by incorporating inter block dependence during the watermarking procedure. The watermarked image demonstrated high perceptual quality and robustness. It also proved to be robust against many common image processing operations and JPEG compression. The scheme not only can verify the authenticity and the integrity of images, but also can locate the illegal modifications.

3-Dimensional Brain MRI Segmentation Based on Multi-Layer Background Subtraction and Seed Region Growing Algorithm

Segmentation of brain MRI in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. In this paper, we propose a robust multi-layer background subtraction technique and seed region growing approach which takes advantages of local texture features represented by local binary patterns (LBP) and photometric invariant color measurements in RGB color space. Due to the use of hybridization of layer-based strategy and seed region growing approach, the approach can model moving background pixels with quasiperiodic flickering as well as background scenes which may vary over time due to the addition and removal of long-time stationary objects. The experiment results prove that in the view of the brain MRI segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

A Novel Approach Based on Multi-Layer Background Subtraction and Seed Region Growing for Sport Graphics Segmentation

Segmentation of motion in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. In this paper, we propose a robust multi-layer background subtraction technique and seed region growing approach which takes advantages of local texture features represented by local binary patterns (LBP) and photometric invariant color measurements in RGB color space. Due to the use of hybridization of layer-based strategy and seed region growing approach, the approach can model moving background pixels with quasiperiodic flickering as well as background scenes which may vary over time due to the addition and removal of long-time stationary objects. The experiment results prove that in the view of the sport image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

Image Segmentation Algorithm Research for Sport Graphics Based on Artificial Life

In this paper, linking with the basic principle of artificial life for image segmentation, on the basis of theory research, the segmented partitions emerge when the state of the lives reaches an equilibrium. The artificial life approach is promising in image processing because it is inherently parallel and coincides with the self-governing biological process. The final segmentation result is achieved at last. The experiments demonstrate the feasibility of the artificial life approach on both intensity images and color images. The experiment results prove that in the view of the sport image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

Fuzzy C-Mean Clustering Image Segmentation Algorithm Research for MRI Sport Graphics Based on Genetic Algorithm

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, a method of the cluster analysis of FCM based on genetic algorithm is proposed. The genetic algorithm is promising in MRI (magnetic resonance image) processing because it is inherently parallel and coincides with the self-governing biological process. Firstly, the approximate optimal solution obtained by the FCM algorithm is taken as the original value, then combined with intensity-texture-position feature space in order to produce connected regions shown in the image, the final segmentation result is achieved at last. The experiment results prove that in the view of the MRI sport image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

Dynamic Image Segmentation for Sport Graphics Based on Wavelet Transform

In this paper, a image segmentation algorithm based on wavelet transform are presented. The proposed image segmentation algorithm performs the segmentation in the combined intensity-texture-position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. This segmentation algorithm is applied to reduced versions of the original images in order to speed-up the completion of the segmentation. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.

Fuzzy Clustering Segmentation Algorithm Research for Sport Graphics Based on Artificial Life

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, the segmented partitions emerge when the state of the lives reaches an equilibrium. The artificial life approach is promising in image processing because it is inherently parallel and coincides with the self-governing biological process. Then combined with intensity-texture-position feature space in order to produce connected regions shown in the image, the final segmentation result is achieved at last. The experiment results prove that in the view of the sport image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

Robust drift-free bit-rate preserving H.264 watermarking

This paper presents a novel method for open-loop watermarking of H.264/AVC bitstreams. Existing watermarking algorithms designed for previous encoders, such as MPEG-2 cannot be directly applied to H.264/AVC, as H.264/AVC implements numerous new features that were not considered in previous coders. In contrast to previous watermarking techniques for H.264/AVC bitstreams, which embed the information after the reconstruction loop and perform drift compensation, we propose a completely new intra-drift-free watermarking algorithm. The major design goals of this novel H.264/AVC watermarking algorithm are runtime-efficiency, high perceptual quality, (almost) no bit-rate increase and robustness to re-compression. The watermark is extremely runtime-efficiently embedded in the compressed domain after the reconstruction loop, i.e., all prediction results are reused. Nevertheless, intra-drift is avoided, as the watermark is embedded in such a way that the pixels used for the prediction are kept unchanged. Thus, there is no drift as the pixels being used in the intra-prediction process of H.264/AVC are not modified. For watermark detection, we use a two-stage cross-correlation. Our simulation results confirm that the proposed technique is robust against re-encoding and shows a negligible impact on both the bit-rate and the visual quality.

Perceptually Driven 3D Progressive Model Selective Transmission over Wireless Network

Realtime streaming 3D model over wireless network is a challenging proposition due to the characteristic of 3D progressive geometry data and wireless network. In this paper, we propose a perceptual-driven 3D progressive model selective transmission method. For the progressive model, the data including the base mesh and a series of vertex splits will be packed into several packets. A novel perceptual quality measure for each packet is presented that takes both the saliency information of the 3D model and the rendering dependency among the packets into accounts. For the dropped packets, a selective transmission method is designed in this paper that will resend or ignore these packets according to their perceptual quality. For retransmitting the dropped packets with higher perceptual quality, a priority-based ARQ, together with a scheduling algorithm which considers not only the perceptual importance of the packets but also their enduring time in the retransmitted buffer is applied at the applica tion layer. Simulation result shows that the 3D model received at the client can achieve better rendering effects with lower tra nsmission delay by our transmission method.

Salient Region Detection and Analysis Based on the Weighted Band-Pass Features

Researches on visual attention mechanism have revealed that the human visual system (HVS) is sensitive to the higher frequency components where they are distinctive from their surroundings by popping out. These attentive components of the scene can be in any form such as edge to texture differences based on the focus of attention of HVS. There are several visual attention computational models that can yield saliency values of attentive regions on the image. Some of these models take advantage of band-pass filter regions on spatial domain by computing center-surround differences with difference of low pass filters. They use either down-sampling that may cause loss of information or constant scale of the filters that may not contain all the necessary saliency information from the image. Therefore, we proposed an efficient and simple saliency detection model with full resolution and high perceptual quality, which outputs several band-pass regions by utilizing Fourier transform to obtain attentive regions edges to textures from the color image. All these detected important information with different bandwidth, then, were fused in a weighted manner by giving more priority to the texture compared to edge based salient regions. Experimental analysis was made for different color spaces and the model was compared with some relevant state of the art algorithms. As a result, the proposed saliency detection model has promising results based on the area under curve (AUC) performance evaluation metric.

Curvelet image denoising of mammogram images

Mammography, the most commonly used diagnostic technique is used for early detection of breast cancer. As mammograms are low contrast and noisy images, it is essential to reduce noise while preserving fine details and edges. In order to obtain efficient diagnosis, a constructive analysis curvelet is used to provide optimal sparse representation of smooth objects and edges. Hence, in this work, a novel noise reduction method using curvelet transform is proposed for digital mammogram images. Here, digital curvelet transform is applied to the mammogram images which are embedded in random, salt and pepper, Poisson, speckle and Gaussian noises. As curvelet coefficients occur at all scales, locations and orientations, proposed thresholding algorithm applied at the expansions lead to better denoising. And it also offers exact reconstruction of images that exhibit higher perceptual quality in recovery of edges. The PSNR values of the enhanced images are higher than related earlier techniques.

Fuzzy C-Mean Clustering Image Segmentation Algorithm Research for Sport Graphics Based on Artificial Life

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, a method of the cluster analysis of FCM based on artificial life is proposed. The artificial life approach is promising in image processing because it is inherently parallel and coincides with the self-governing biological process. Firstly, the approximate optimal solution obtained by the FCM algorithm is taken as the original value, then combined with intensity-texture-position feature space in order to produce connected regions shown in the image, the final segmentation result is achieved at last. The experiment results prove that in the view of the sport image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

FCM Segmentation Algorithm Research for CT Cerebrovascular Medical Image Based on Sober Extraction Algorithm

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, a method of the cluster analysis of FCM based on sober extraction algorithm is proposed. To insure the quality of image reconstruction and the edge information extraction, the characters of sober operator is analyzed. Firstly, the approximate optimal solution obtained by the improved FCM algorithm is taken as the original value, then combined with intensity-texture-position feature space in order to produce connected regions shown in the image. The final segmentation result is achieved at last. The experiment results prove that in the view of the image segmentation, this segmentation algorithm based on sober extraction algorithm provides fast segmentation with high perceptual segmentation quality.

A collusion attack optimization strategy for digital fingerprinting

Collusion attack is a cost-efficient attack for digital fingerprinting. In this article, we propose a novel collusion attack strategy, Iterative Optimization Collusion Attack (IOCA) , which is based upon the gradient attack and the principle of informed watermark embedding. We evaluate the performance of the proposed collusion attack strategy in defeating four typical fingerprinting schemes under a well-constructed evaluation framework. The simulation results show that the proposed strategy performs more effectively than the gradient attack, and adopting no more than three fingerprinted copies can sufficiently collapse examined fingerprinting schemes. Meanwhile, the content resulted from the proposed attack still preserves high perceptual quality.

A Cross-Layer Design for Perceptual Optimization Of H.264/SVC with Unequal Error Protection

Delivering high perceptual quality video over wireless channels is challenging due to the changing channel quality and the variations in the importance of one source packet to the next for the end-user's perceptual experience. Leveraging perceptual metrics in concert with link adaptation to maximize perceptual quality and satisfy real-time delay constraints is largely unexplored. We introduce an APP/MAC/PHY cross-layer architecture that enables optimizing perceptual quality for delay-constrained scalable video transmission. We propose an online QoS-to-QoE mapping technique to quantify the loss visibility of packets from each video layer using the ACK history and perceptual metrics. At the PHY layer, we develop a link adaptation technique that uses the QoS-to-QoE mapping to provide perceptually-optimized unequal error protection per layer according to packet loss visibility. At the APP layer, the source rate is adapted by selecting the set of temporal and quality layers to be transmitted based on the channel statistics, source rates, and playback buffer state. The proposed cross-layer optimization framework allows the channel to adapt at a faster time scale than the video codec. Furthermore, it provides a tradeoff between playback buffer occupancy and perceptual quality. We show that the proposed architecture prevents playback buffer starvation, provides immunity against short-term channel fluctuations, regulates the buffer size, and achieves a 30% increase in video capacity versus throughput-optimal link adaptation.

Quantifying Video-QoE Degradations of Internet Links

With the proliferation of multimedia content on the Internet, there is an increasing demand for video streams with high perceptual quality. The capability of present-day Internet links in delivering high-perceptual-quality streaming services, however, is not completely understood. Link-level degradations caused by intradomain routing policies and inter-ISP peering policies are hard to obtain, as Internet service providers often consider such information proprietary. Understanding link-level degradations will enable us in designing future protocols, policies, and architectures to meet the rising multimedia demands. This paper presents a trace-driven study to understand quality-of-experience (QoE) capabilities of present-day Internet links using 51 diverse ISPs with a major presence in the US, Europe, and Asia-Pacific. We study their links from 38 vantage points in the Internet using both passive tracing and active probing for six days. We provide the first measurements of link-level degradations and case studies of intra-ISP and inter-ISP peering links from a multimedia standpoint. Our study offers surprising insights into intradomain traffic engineering, peering link loading, BGP, and the inefficiencies of using autonomous system (AS)-path lengths as a routing metric. Though our results indicate that Internet routing policies are not optimized for delivering high-perceptual-quality streaming services, we argue that alternative strategies such as overlay networks can help meet QoE demands over the Internet. Streaming services apart, our Internet measurement results can be used as an input to a variety of research problems.

Determination of Eco-tourism potential of Düzce Ugursuyu and Aksu Basins and Landscape Management

Determination of eco-tourism potential in the areas with high eco-tourism potential and formation of a landscape management plan are the issues that have been studied in terms of different perspectives recently. Eco-tourism causes more harm than mass tourism in the cases when eco-tourism is not planned well and management factor is ignored. In this study, eco-tourism potential has been determined in Duzce Ugursuyu and Aksu basins which abound in natural and cultural landscape values. The method of the study is to map major and additional sources in the research area to identify, protect and improve the important patterns with high perceptual quality and to make analyses by creating data bases in the environment of geographical information systems (GIS). Additionally, a landscape management method has been developed with the help of the questionnaires made with the native society leaders about the cultural structure and education level of the locals, their possible contributions to eco-tourism activities and their expectations from eco-tourism. The analyses of the major and additional sources of the area in the environment of GIS, determination of the problems and identification of regional and national connections of landscape management have shown that it would make important contributions to the sustainable use of the landscape management method. Key words: Landscape planning, landscape management, ecotourism, Basin Management, Duzce.