Depth quality of stereoscopic three-dimensional (S3D) videos is a significant factor which directly affects the quality of experience (QoE) associated with 3D video applications and services. Nevertheless, there remain limited reports on the investigation of depth perception and depth quality evaluation of S3D videos, which impedes further advancement and deployment of 3D video technology. This paper reports a series of subjective experiments which have been conducted to investigate the depth perception and its related properties of the human visual system (HVS) using S3D video compressed by the H.264/AVC standard. The experimental results reveal that the HVS response in depth perception varies at different frequencies and in varying orientations, and the distortions introduced by video coding can cause the loss of and/or variation in depth perception. By integration of binocular and monocular features (BM) extracted from left and right views of S3D video with respect to depth perception, a depth quality assessment model, herein referred to as BM-DQAM, is devised by training these stereoscopic and spatial orientation structural features with a support vector regression model. It is shown that the BM-DQAM provides a novel no-reference metric for evaluation of the depth quality of S3D videos. Based on two publicly available 3D video databases and the proposed depth perception assessment database, the experimental results show that the BM-DQAM has demonstrated better performance in assessing the depth quality in S3D video viewing than that of other metrics reported in the published literatures, correlating well with the HVS response in the depth perception assessment experiment.

Video surveillance and its applications have become increasingly ubiquitous in modern daily life. In video surveillance system, video coding as a critical enabling technology determines the effective transmission and storage of surveillance videos. In order to meet the real-time or time-critical transmission requirements of video surveillance systems, the low-delay (LD) configuration of the advanced high efficiency video coding (HEVC) standard is usually used to encode surveillance videos. The coding efficiency of the LD configuration is closely related to the quantization parameter (QP) cascading technique which selects or determines the QPs for encoding. However, the quantization parameter cascading (QPC) technique currently adopted for the LD configuration in HEVC test model (i.e., HM) is not optimized since it has not taken full account of the reference dependency in coding. In this paper, an efficient QPC technique for surveillance video coding, referred to as QPC-SV, is proposed, considering all inter reference frames under the LD configuration. Experimental results demonstrate the efficacy of the proposed QPC-SV. Compared with the default configuration of QPC in the HM, the QPC-SV achieves significant rate-distortion performance gain with average BD-rates of -9.35% and -9.76% for the LDP and LDB configurations, respectively.

Rate control is a key technique for video communication systems. The aim of rate control is to transmit the best possible quality video sequences under various restrictions, such as channel bandwidth, buffer capacity, maximum time delay allowed for a given service, and so on. The λ domain rate control technique (λ-RC) has been integrated into the latest High Efficiency Video Coding standard (H.265/HEVC) test model, due to its accurate bit estimation and high rate-distortion performance. However, it is found that the λ-RC is not the optimal choice under the random-access configuration. When the random-access configuration is used, pictures are organized into temporal layers, where pictures in different layers are of different importance in terms of prediction. In this paper, a picture level lambda domain rate control technique for the randomaccess configuration in H.265/HEVC is proposed. The influence of temporal layers is effectively considered in the proposed algorithm referred to as TL-λ-PRC. Experimental results verify that the proposed TL-λ-PRC is efficient in coding performance and accurate in bit estimation. Compared with the λ-RC with the fixed ration bit allocation which has been implemented in the test model of H.265/HEVC (HM 14.0), TL-λ-PRC achieves an average reduction of 4.10% and 3.49% for slow motion and fast motion sequences, respectively, in BD-rate (Bjøntegaard-Delta bitrate) with more accurate bit estimation. The performance of different algorithms in terms of algorithm complexity and quality fluctuation are also carefully analyzed in this contribution.

Currently, as one of the most important video coding structures, the low-delay (LD) configuration has been widely adopted in the video communication systems, such as video surveillance and video conferencing systems. The rate-distortion optimization (RDO) is also a crucial technique for improving the performance of video coding. The RDO performance of H.265/HEVC with the LD configuration is significantly affected by the temporal dependence. This paper examines the dependence of rate and distortion among temporal layers in H.265/HEVC with the LD configuration. An efficient RDO method is then proposed based on temporal dependence, where the optimized Lagrangian multipliers are estimated by adaptively selecting the scaling factors according to the video motion characteristics pertaining to different temporal layers. The experimental results show that compared with the RDO method adopted in the H.265/HEVC test model (i.e., HM), the proposed RDO method can achieve the average BD-rate reductions of 6.4% and 5.8% for the LD P and LD B configurations, respectively.

The popularity of HTTP-based video streaming services has been increasing in recent years. The quality of HTTP-based video streaming services is measured by the user's quality of experience (QoE). Perceptual quality (PQ), a strong indicator of the QoE, has been widely studied in the literature. Specifically, previous studies primarily focused on modeling the overall perceptual quality at the end of the viewing process. The specific influence of each event during the viewing process was neglected. Since the PQ is a function of time, which models the human perception of audiovisual services, this contribution focuses on investigating the time-varying feature of human perception. In this paper, the viewing timeline of subjects is divided into rebuffering and playback. An event-based perceptual quality assessment (EPQ) framework is introduced, which can assess the PQ of interrupted HTTP-based video streaming at any point in time. To understand the human perception of video streams with playback interruptions, a subjective quality assessment experiment was designed to obtain the PQ at a series of points in time. A between-subjects design was adopted in which each subject can view video content without repetition. Based on the experimental results, a PQ assessment model was proposed to predict the change in PQ (i.e., ΔPQ) after each rebuffering and playback. The overall PQ at any point in time is calculated as the summation of the ΔPQs of all the previous events. The experimental results demonstrated that the EPQ-based model outperforms the rebuffering components contained in the ITU-T Rec. P.1201.1, P.1201 Amd. 2 and P.1203.3 in our database.

A subjective assessment method is described to determine picture quality levels in the supra-threshold region for processed images, with reference to their original counterparts, based on just-noticeable difference (JND) detection experiment. It has been found that the range of JND levels is dependent on picture contents and can be predicted as a function of texture masking factor computed in the pixel domain. The experimental data obtained also reveal that relationship of JND levels in the supra-threshold region and the MSE (mean squared error) can be approximated by a linear function whose slope is modeled as a function of edge and texture contrast masking factors. The model is devised to predict JND levels which provide subjective picture quality rating discernible by human viewers and can be used for visual quality regulated image/video coding, as well as evaluating the capacity of existing objective metrics in predicting picture quality and/or distortion relative to JND based quality/distortion rating categories.

Quality of experience (QoE) has significant influence on whether or not a user will choose a service or product in the competitive era. For multimedia services, there are various factors in a communication ecosystem working together on users, which stimulate their different senses inducing multidimensional perceptions of the services, and inevitably increase the difficulty in measurement and estimation of the user's QoE. In this paper, a user-centric objective QoE evaluation model (QAVIC model for short) is proposed to estimate the user's overall QoE for audiovisual services, which takes account of perceptual audiovisual quality (QAV) and user interest in audiovisual content (IC) amongst influencing factors on QoE such as technology, content, context, and user in the communication ecosystem. To predict the user interest, a number of general viewing behaviors are considered to formulate the IC evaluation model. Subjective tests have been conducted for training and validation of the QAVIC model. The experimental results show that the proposed QAVIC model can estimate the user's QoE reasonably accurately using a 5-point scale absolute category rating scheme.

At low bit-rates, video coding in the hierarchical prediction structure (HPS) using the quantization parameter cascading strategy will introduce the temporal pumping artifact (TPA). TPA presents itself as a stumbling visual effect and is caused by severe quality fluctuations among adjacent pictures. This paper proposes a frame level metric for just noticeable temporal pumping artifact (JNTPA) relying on the characteristics of temporal-spatial masking in the human visual system (HVS). The proposed metric quantitatively determines the TPA when it becomes visible. The experiments have demonstrated that the estimated JNTPA values for different videos using the proposed metric are in line with the HVS perception. An accurate estimation of the JNTPA can be used in video coding for avoiding the annoying TPA, and in perceptual quality evaluation for videos.

A method for subjective assessment of picture quality based on supra-threshold levels is presented. In the proposed approach, picture quality is graded relative to supra-threshold points in a recursive manner as opposed to the common absolute category rating scale. In addition, assessments were carried out with full resolution and sub-resolution pictures to measure the presence of quality disparity. Data collected from subjective tests were used to measure the capability of four human visual system based objective metric in quantifying picture quality relative to supra-threshold levels.

Wi-Fi and smartphone based location tracking technologies have been widely applied in location based services (LBS). This research is a part of an Australian Research Council (ARC) project required by a large global shopping mall company located in Australia. It aims to develop an effective customer tracking approach for acquiring shopping behavior of customers and providing them better services. Currently, the log data provided by the company only recorded one Wi-Fi connection at a time for one smartphone user. This makes most of the conventional tracking and positioning methods inapplicable. In order to solve this problem, a new approach was developed, which takes advantage of both the cell of origin (CoO) method and the fingerprinting method. Its implementation mainly includes two phases: 1) the cell determination phase, including partitioning of the floors of the shopping mall using a Voronoi diagram and determining a unique cell for each wireless access point (AP), adjusting the cells manually according to the real floor layout, obstructions as well as an on-site observing and calibrating process. An AP-shop/room matching table and other relevant information are stored in a database; and 2) the user location tracking phase, including determination of the cell that the customer locates in according to the detected AP linked with the customer's mobile phone. A back-tracing method is also used to improve the tracking performance. This new approach was tested in a large shopping-mall-like space covering an area of 1000 square meters, containing 8 simulated shops and 11 APs. The results showed a 96% correct detection rate whilst the 4% erroneous detections occurred when a user was near the cell boundaries. This new approach is also easy to implement and integrate with other location tracking systems.

One- and two-way communication with digital compressed visual signals is now an integral part of the daily life of millions. Such commonplace use has been realized by decades of advances in visual signal compression. The design of effective, efficient compression and transmission strategies for visual signals may benefit from proper incorporation of human visual system (HVS) characteristics. This paper overviews psychophysics and engineering associated with the communication of visual signals. It presents a short history of advances in perceptual visual signal compression, and describes perceptual models and how they are embedded into systems for compression and transmission, both with and without current compression standards.

This paper introduces a tensor perceptual color framework (TPCF) for facial expression recognition (FER), which is based on information contained in color facial images. The TPCF enables multi-linear image analysis in different color spaces and demonstrates that color components provide additional information for robust FER. Using this framework, the components (in either RGB, YCbCr, CIELab or CIELuv space) of color images are unfolded to two-dimensional (2- D) tensors based on multi-linear algebra and tensor concepts, from which the features are extracted by Log-Gabor filters. The mutual information quotient (MIQ) method is employed for feature selection. These features are classified using a multi-class linear discriminant analysis (LDA) classifier. The effectiveness of color information on FER using low-resolution and facial expression images with illumination variations is assessed for performance evaluation. Experimental results demonstrate that color information has significant potential to improve emotion recognition performance due to the complementary characteristics of image textures. Furthermore, the perceptual color spaces (CIELab and CIELuv) are better overall for facial expression recognition than other color spaces by providing more efficient and robust performance for facial expression recognition using facial images with illumination variation.

An approach to perceptual coding at the threshold level, for supporting the Digital Cinema System Specification (DCSS) standard, is presented. The proposed Perceptual DCSS Coder (PDC) follows the approach of the Visually Lossless Grey scale Image Coder (VLGIC) of adapting Human Visual System (HVS) modelling to the JPEG2000 coder, but extends it to multi-frame application for DCSS. This PDC first applies pixel-domain temporal filtering to video frames. This is followed by the encoding of each colour component of individual frame with the VLGIC. The VLGIC is a JPEG2000 compliant coder that utilises a HVS based vision model to identify and remove imperceptible information in images/frames prior to coding operations. Experimental results show that the PDC provides improvement, in bitrate, over JPEG2000 reversible (lossless) mode. Furthermore, subjective assessments with 16 observers reveal no perceptible differences between PDC coded frames and their respective originals.

Temporal fluctuation artifact is often observed in digitally compressed video. However, the fluctuation intensity cannot be correctly measured by the traditional image/video quality metric, e.g., the peak signal-to-noise ratio (PSNR), which only addresses on the quality of a single image. Although there are several metrics proposed for temporal fluctuation measurement, e.g., the sum of squared differences (SSD) and motion compensated SSD (MCSSD), these first difference based algorithms may falsely treat a smoothly continuous change of pixels as the temporal fluctuation artifact. To overcome this problem, this contribution proposes a second difference based temporal metric, named the motion estimated mean scaled absolute second difference (MEMSASD) The performance of the MEMSASD is examined using a number of video sequences with varying degrees of temporal fluctuation, which are generated by an H.264/AVC compliant codec. Compared with existing metrics such as the PSNR, the SSD and the MCSSD, the results of the proposed metric better reflect the temporal fluctuation intensity.

The H.264/advanced video coding standard applies a spatial loop-filter in both encoder and decoder to reduce the blocking artifact. However, the temporal fluctuation artifact is still noticeable between intra-coded frames or between an intra (I)-frame and its preceding or subsequent inter-prediction (P) frames. This letter proposes a temporal postfilter using a robust statistical model to reduce the temporal fluctuation and to preserve the motion sharpness of the reconstructed video. The novelty is the implementation of a simplified robust statistical model as well as the Huber statistical model for the temporal fluctuation reduction. Performance evaluations of fluctuation reduction have been conducted using the proposed robust statistical temporal filter (RSTF), the Huber filter, and three other temporal filtering techniques, in terms of a temporal impairment metric (i.e., the sum of squared differences), and a spatial quality metric (i.e., the peak-signal-to-noise ratio). Consistent improvements of the temporal fluctuation reduction and the perceptual picture quality produced by the RSTF are demonstrated using the aforementioned performance criteria, and can be best appreciated by viewing the sequences on professional broadcasting television monitors.

A no-reference (NR) quality measure for networked video is introduced using information extracted from the compressed bit stream without resorting to complete video decoding. This NR video quality assessment measure accounts for three key factors which affect the overall perceived picture quality of networked video, namely, picture distortion caused by quantization, quality degradation due to packet loss and error propagation, and temporal effects of the human visual system. First, the picture quality in the spatial domain is measured, for each frame, relative to quantization under an error-free transmission condition. Second, picture quality is evaluated with respect to packet loss and the subsequent error propagation. The video frame quality in the spatial domain is, therefore, jointly determined by coding distortion and packet loss. Third, a pooling scheme is devised as the last step of the proposed quality measure to capture the perceived quality degradation in the temporal domain. The results obtained by performance evaluations using MPEG-4 coded video streams have demonstrated the effectiveness of the proposed NR video quality metric.

A perceptual color image coder (PCIC) is presented for the YC(b)C(r) color space within the framework of JPEG2000. This coder employs a vision model based perceptual distortion metric (PDM) to approximate perceived error for rate-distortion (R-D) optimization in order to maximize the visual quality of coded images. The vision model employed in the PCIC is structurally based on an existing monochromatic multichannel vision model, which is extended for color image coding. Subjective tests with 30 viewers show that the PCIC provides superior picture quality at low to intermediate bitrates in comparison with a JPEG2000 compliant coder employing the mean squared error (MSE) and the visual distortion metric (Cvis) as distortion measures, respectively.

A geometric features-based filtering technique, named as the adaptive geometric features based filtering technique (AGFF), is presented for removal of impulse noise in corrupted color images. In contrast with the traditional noise detection techniques where only 1-D statistical information is used for noise detection and estimation, a novel noise detection method is proposed based on geometric characteristics and features (i.e., the 2-D information) of the corrupted pixel or the pixel region, leading to effective and efficient noise detection and estimation outcomes. A progressive restoration mechanism is devised using multipass nonlinear operations which adapt to the intensity and the types of the noise. Extensive experiments conducted using a wide range of test color images have shown that the AGFF is superior to a number of existing well-known benchmark techniques, in terms of standard image restoration performance criteria, including objective measurements, the visual image quality, and the computational complexity.

An Adaptive geometric features based filtering (AGFF) technique with a low computational complexity is proposed for removal of impulse noise in corrupted color images. The effective and efficient detection is based on geometric characteristics and features of the corrupted pixel and/or the pixel region. A progressive restoration mechanism is devised using multi-pass non-linear operations. Through extensive experiments conducted using a wide range of test color images, the proposed filtering technique has demonstrated superior performance to that of well-known benchmark techniques, in terms of objective measurements, the visual image quality and the computational complexity.

This paper introduces a novel image enhancement methodology driven by both global and local processes. This methodology, based on the parameters controlled hybrid approach which integrates the advantages of point operations with local enhancement techniques, can enhance, simultaneously, the overall contrast and the sharpness of an image and increase, especially, the visibility of specified portions or aspects of the image. The methodology was compared with other classical image enhancement techniques, such as linear contrast stretching and histogram equalization. Results obtained in terms of subjective evaluation have shown the superiority of the proposed approach.

The H.264/AVC coding standard reduces the blocking artifact by applying a spatial loop-filter in the encoder and in the decoder. However, the temporal fluctuation or flickering artifact is still noticeable between intra-coded frames or between an intra (I) frame and the preceding or subsequent inter prediction (P) frames. This paper proposes a non-linear temporal filter to reduce the flickering artifact and preserve image sharpness of the reconstructed video, by using a robust prior model. Performance of the flickering reduction with proposed filter is evaluated by a temporal metric, namely the sum of square difference (SSD), and the traditional measure, the peak signal to noise ratio (PSNR).

A partition-based adaptive vector filter is proposed for the restoration of corrupted digital color images. The novelty of the filter lies in its unique three-stage adaptive estimation. The local image structure is first estimated by a series of center-weighted reference filters. Then the distances between the observed central pixel and estimated references are utilized to classify the local inputs into one of preset structure partition cells. Finally, a weighted filtering operation, indexed by the partition cell, is applied to the estimated references in order to restore the central pixel value. The weighted filtering operation is optimized off-line for each partition cell to achieve the best tradeoff between noise suppression and structure preservation. Recursive filtering operation and recursive weight training are also investigated to further boost the restoration performance. The proposed filter has demonstrated satisfactory results in suppressing many distinct types of noise in natural color images. Noticeable performance gains are demonstrated over other prior-art methods in terms of standard objective measurements, the visual image quality and the computational complexity.

A novel perceptually lossless coder is presented for the compression of medical images. Built on the JPEG 2000 coding framework, the heart of the proposed coder is a visual pruning function, embedded with an advanced human vision model to identify and to remove visually insignificant/irrelevant information. The proposed coder offers the advantages of simplicity and modularity with bit-stream compliance. Current results have shown superior compression ratio gains over that of its information lossless counterparts without any visible distortion. In addition, a case study consisting of 31 medical experts has shown that no perceivable difference of statistical significance exists between the original images and the images compressed by the proposed coder.

A new adaptive backpropagation (BP) algorithm based on Lyapunov stability theory for neural networks is developed in this paper. It is shown that the candidate of a Lyapunov function V(k) of the tracking error between the output of a neural network and the desired reference signal is chosen first, and the weights of the neural network are then updated, from the output layer to the input layer, in the sense that deltaV(k) = V(k) - V(k - 1) < 0. The output tracking error can then asymptotically converge to zero according to Lyapunov stability theory. Unlike gradient-based BP training algorithms, the new Lyapunov adaptive BP algorithm in this paper is not used for searching the global minimum point along the cost-function surface in the weight space, but it is aimed at constructing an energy surface with a single global minimum point through the adaptive adjustment of the weights as the time goes to infinity. Although a neural network may have bounded input disturbances, the effects of the disturbances can be eliminated, and asymptotic error convergence can be obtained. The new Lyapunov adaptive BP algorithm is then applied to the design of an adaptive filter in the simulation example to show the fast error convergence and strong robustness with respect to large bounded input disturbances.

A new adaptive vector filter is proposed for restoring color images corrupted by impulse noise. The local image structure is estimated by a series of central weighted vector median filtering operations. Then a classification process is applied to map the local estimate errors into a group of mutually exclusive structure partition cells. For each partition, an optimal weighted filter is applied to provide the best image structure restoration. The new filter has demonstrated satisfactory results in suppressing various distinct types of impulse noise. Noticeable performance gains have been demonstrated over other existing methods in terms of objective measures and perceptual quality.

A novel objective no-reference metric is proposed for video quality assessment of digitally coded videos containing natural scenes. Taking account of the temporal dependency between adjacent images of the videos and characteristics of the human visual system, the spatial distortion of an image is predicted using the differences between the corresponding translational regions of high spatial complexity in two adjacent images, which are weighted according to temporal activities of the video. The overall video quality is measured by pooling the spatial distortions of all images in the video. Experiments using reconstructed video sequences indicate that the objective scores obtained by the proposed metric agree well with the subjective assessment scores.

A robust structure-adaptive hybrid vector filter is proposed for digital color image restoration in this paper. At each pixel location, the image vector (i.e., pixel) is first classified into several different signal activity categories by applying a modified quadtree decomposition to luminance component (image) of the input color image. A weight-adaptive vector filtering operation with an optimal window is then activated to achieve the best tradeoff between noise suppression and detail preservation. Through extensive simulation experiments conducted using a wide range of test color images, the filter has demonstrated superior performance to that of a number of well known benchmark techniques, in terms of both standard objective measurements and perceived image quality, in suppressing several distinct types of noise commonly considered in color image restoration, including Gaussian noise, impulse noise, and mixed noise.

A novel window adaptive hybrid filter for the removal of different types of noise which contaminate color images is proposed. At each pixel location, the image vector is first classified into several different signal activity areas through a modified quadtree decomposition on the luminance of the input image, with only one empirical parameter required to be controlled. Then, an optimal window and weight adaptive vector filtering operation is activated for the best tradeoff between noise suppression and detail preservation. The proposed filter has demonstrated superior performance in suppressing several distinctive types of color image noise, which include Gaussian, impulse, and mixed noise. Significant improvements have been achieved in terms of standard objective measurements as well as the perceived image quality.

A novel perceptual image coder of gray level images is presented. This coder adopts the EBCOT coding structure employed in the JPEG2000 image coding standard , coupled with an advanced vision model based perceptual distortion measure for rate-distortion optimization to produce coded images with improved visual quality. The proposed coder maintains the EBCOT coding features such as scalability and is fully bitstream compliant with the JPEG2000 standard. Results of subjective evaluations, using comparative forced-choice subjective tests, demonstrated the superior performance of the proposed perceptual coder over the the EBCOT/JPEG2000 benchmark coders.

This paper evaluates a perceptual impairment measure for ringing artifacts, which are common in hybrid MC/DPCM/DCT coded video, as a predictor of the mean opinion score (MOS) obtained in the standard subjective assessment. The perceptual ringing artifacts measure is based on a vision model and a ringing distortion region segmentation algorithm, which is converted into a new perceptual ringing distortion metric (PRDM) on a scale of 0 to 5. This scale corresponds to a modified double-stimulus impairment scale variant II (DSIS-II) method. The Pearson correlation, the Spearman rank order correlation and the average absolute error are used to evaluate the performance of the PRDM compared with the subjective test data. The results show a strong correlation between the PRDM and the MOS with respect to ringing artifacts.

This paper evaluates a perceptual impairment measure for ringing artifacts, which are common in hybrid MC/DPCM/DCT coded video, as a predictor of the mean opinion score (MOS) obtained in the standard subjective assessment. The perceptual ringing artifacts measure is based on a vision model and a ringing distortion region segmentation algorithm, which is converted into a new perceptual ringing distortion metric (PRDM) on a scale of 0 to 5. This scale corresponds to a modified double-stimulus impairment scale variant II (DSIS-II) method. The Pearson correlation, the Spearman rank order correlation and the average absolute error are used to evaluate the performance of the PRDM compared with the subjective test data. The results show a strong correlation between the PRDM and the MOS with respect to ringing artifacts.

A novel perceptual image coder of grey level images is presented. This coder is an improved version of the coder by Tan et al with better optimised parameters featuring a local contrast sensitivity function, intra-frequency masking and inter-orientation masking functions for perceptual error modelling. The architecture of the proposed coder follows that of the state-of-the-art EBCOT by Taubman and adopted by the JPEG2000 standard as the core coding structure. The overall perceptual performance improvement of the proposed coder is noticeable compared with the EBCOT coder with the MSE and CVIS error measures.

In this paper we present a robust and efficient switch-based filtering technique that makes use of a multiple window noise detection scheme. The proposed method performs exceptionally well for both impulse and Gaussian type noise, with a minimum amount of computational expense incurred. All parameters required for the MWC detection are trained off-line using a maximum-likelihood estimator, so no a prior knowledge of the noise type or image characteristics are required. As the results show, the new technique demonstrates a marked performance gain over existing state of the art methods.

In this paper investigations are conducted to simplify and refine a vision-model-based video quality metric without compromising its prediction accuracy. Unlike other vision-model-based quality metrics, the proposed metric is parameterized using subjective quality assessment data recently provided by the Video Quality Experts Group. The quality metric is able to generate a perceptual distortion map for each and every video frame. A perceptual blocking distortion metric (PBDM) is introduced which utilizes this simplified quality metric. The PBDM is formulated based on the observation that blocking artifacts are noticeable only in certain regions of a picture. A method to segment blocking dominant regions is devised, and perceptual distortions in these regions are summed up to form an objective measure of blocking artifacts. Subjective and objective tests are conducted and the performance of the PBDM is assessed by a number of measures such as the Spearman rank-order correlation, the Pearson correlation, and the average absolute error The results show a strong correlation between the objective blocking ratings and the mean opinion scores on blocking artifacts.

A novel method is proposed for image interpolation. It is assumed that the pixel correlation between local regions across scales would remain similar. In addition, this a priori similarity could be extracted from a set of available image data that have the same content but different resolutions. A simple architecture is devised to estimate the correlation efficiently, which is then used to predict the unknown pixel values in a high-resolution image. Evaluation shows a promising performance of the proposed algorithm.

Previous median-based impulse detection strategies tend to work well for fixed-valued impulses but poorly for random-valued impulse noise, or vice versa. This letter devises a novel adaptive operator, which forms estimates based on the differences between the current pixel and the outputs of center-weighted median (CWM) filters with varied center weights. Extensive simulations show that the proposed scheme consistently works well in suppressing both types of impulses with different noise ratios.

The problem of designing the weights for recursive L-filters optimized by the least mean square (LMS) algorithm is addressed. The coefficients derived for nonrecursive filtering are not optimal for recursive implementation, where the estimate of current pixel depends on the past outputs of the filter. To combat this, analogous to the design of adaptive IIR filters, the optimization scheme referred to as equation-error formulation is employed. The recursive filter performs better in suppressing noise than its nonrecursive counterpart.

This paper proposes a novel postprocessing technique for reducing blocking artifacts in low-bit-rate transform-coded images. The proposed approach works in the transform domain to alleviate the accuracy loss of transform coefficients, which is introduced by the quantization process. The masking effect in the human visual system (HVS) is considered, and an adaptive weighting mechanism is then integrated into the postfiltering. In low-activity areas, since blocking artifacts appear to be perceptually more detectable, a large window is used to efficiently smooth out the artifacts. In order to preserve image details, a small mask, as well as a large central weight, is employed for processing those high-activity blocks, where blocking artifacts are less noticeable due to the masking ability of local background. The quantization constraint is finally applied to the postfiltered coefficients. Experimental results show that the proposed technique provides satisfactory performance as compared to other postfilters in both objective and subjective image quality.

An adaptive median based filter is proposed for removing noise from images. Specifically, the observed sample vector at each pixel location is classified into one of M mutually exclusive partitions, each of which has a particular filtering operation. The observation signal space is partitioned based an the differences defined between the current pixel value and the outputs of CWM (center weighted median) filters with variable center weights. The estimate at each location is formed as a linear combination of the outputs of those CWM filters and the current pixel value. To control the dynamic range of filter outputs, a location-invariance constraint is imposed upon each weighting vector. The weights are optimized using the constrained LMS (least mean square) algorithm. Recursive implementation of the new filter is then addressed. The new technique consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and it also works satisfactorily in reducing Gaussian noise as well as mixed Gaussian and impulse noise.

Images are often corrupted by impulse noise due to a noisy sensor or channel transmission errors. The goal of removing impulse noise is to suppress the noise while preserving the integrity of edge and detail information associated with the original image. In this paper, a generalized framework for median filtering based on a switching scheme, called multi-state median (MSM) filter, is proposed. By using a simple thresholding operation, the output of the proposed MSM filter is adaptively switched among those of a group of centre weighted median (CWM) filters having different centre weights. As a result, the MSM filter is equivalent to an adaptive CWM filter with a space varying center weight which is dependent on local signal characteristics. The effectiveness in noise suppression and detail preservation of the proposed filtering technique has been evaluated by extensive simulations, showing superior performance to other median based filters.

The discrete wavelet transform (DWT) gives a compact multiscale representation of signals and provides a hierarchical structure for signal processing. It has been assumed the DWT can fairly well decorrelate real-world signals. However a residual dependency structure still remains between wavelet coefficients. It has been observed magnitudes of wavelet coefficients are highly correlated, both across the scale and at neighboring spatial locations. In this paper we present a wavelet folding technique, which folds wavelet coefficients across the scale and removes the across-the-scale dependence to a larger extent. It produces an even more compact signal representation and the energy is more concentrated in a few large coefficients. It has a great potential in applications such as image compression.

This paper proposes a novel adaptive filter based on the impulse rejecting mechanism, where detected impulses are filtered and noise-free pixels are left unaltered. Previous impulse detection strategies based on thresholding operations tend to work well for large, fixed-valued impulses but poorly for random-valued impulse noise, or vice versa. The objective of this work is to utilize the center weighted median (CWM) filters with variable center weights to define a more general operator, which forms estimates according to the differences defined between the outputs of CWM filters and the current pixel in consideration. As compared with existing schemes, the proposed filter consistently performs well in suppressing both types of impulse noise while still employing a simple structure. Better performance has been achieved by the new filter in restoring a variety of images corrupted with different noise ratios.

This paper presents an overview in the area of multimedia computing and communications. It raises some of the issues in processing and communication of real-time multimedia materials which need to be addressed.

This paper shows that using the Loeffler's (see Proc. IEEE ICASSP, vol.2, p.988-91, 1989) one-dimensional (1-D) 8-point discrete cosine transform (DCT) algorithm in Cho and Lee's (see ibid., vol.38, p.297-305, 1991) two-dimensional (2-D) 8/spl times/8-point DCT algorithm will result in an optimal algorithm in the sense of multiplicative complexity theory.

A neural-network-based adaptive tracking control scheme is proposed for a class of nonlinear systems in this paper. It is shown that RBF neural networks are used to adaptively learn system uncertainty bounds in the Lyapunov sense, and the outputs of the neural networks are then used as the parameters of the controller to compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero. A simulation example is performed in support of the proposed neural control scheme.

A mew generalized block-edge impairment metric (GBIM) is presented in this paper as a quantitative distortion measure for blocking artifacts in digital video and image coding. This distortion measure does not require the original image sequence as a comparative reference, and is found to be consistent with subjective evaluation.