Next-generation communication networks are revolutionizing digital publishing through intelligent content distribution and collaborative optimization capabilities. However, existing federated learning approaches face fundamental limitations, including trusted third-party dependencies, excessive communication overhead, and vulnerability to collusion attacks between servers and participants. This paper introduces VHFL-DP, a verifiable homomorphic federated learning framework for digital publishing environments operating within 6G network infrastructures. The framework addresses critical privacy and scalability challenges through four key innovations: a distributed cryptographic key generation protocol that eliminates trusted third-party requirements, Chinese remainder theorem-based dimensionality reduction, auxiliary validation nodes that enable independent verification with constant-time complexity, and an intelligent incentive mechanism that rewards digital publishing platforms based on objective contribution quality metrics. Experimental evaluation on MNIST and Amazon reviews datasets across six baseline methods demonstrates that VHFL-DP achieves superior performance with accuracy improvements of 4.2% over the best baseline method. The framework maintains constant verification time ranging from 2.73 to 2.91 seconds regardless of platform count, increasing from ten to fifty, or dropout rates reaching thirty percent. Security evaluation reveals strong resilience with only 2.4 percentage point accuracy degradation under poisoning attacks compared to 6.7-7.0 points for baseline method, inference attack success near random guessing at 51.3%, and 92.4% successful aggregation under Byzantine adversaries.

Medical supply chains face critical challenges in balancing transparent data access with privacy protection while maintaining environmental sustainability. Organizations must share crucial information like production specifications and environmental monitoring data, yet protect sensitive information such as proprietary formulations and patient records. Traditional access control mechanisms use centralized approaches that lack transparency, create single points of failure, and cannot adapt to the complex data sharing requirements of Artificial Intelligence (AI)-based Internet of Things (IoT) (AIoT) -enhanced green medical supply chains. To address these challenges, we propose green blockchain hierarchical AIoT-enhanced medical supply chain (G-BHAIMS), a novel framework that enables secure, energy-efficient data access control. G-BHAIMS employs a multi-chain architecture that segregates sensitive medical data storage from access control operations, while integrating environmental monitoring through AIoT devices. Our framework introduces a hierarchical access control model with data classification based on sensitivity levels and ecological impact factors, implemented through energy-efficient smart contracts. Experimental results demonstrate that G-BHAIMS achieves a 98.5% correct access decision rate, outperforming the best baseline method by 2.5 percentage points. The framework maintains an average execution time of 22ms (21.4% faster than comparable systems) and throughput above 95 TPS under heavy loads. Most significantly, G-BHAIMS reduces energy consumption by 17.8% and decreases carbon emissions by 16.1% compared to the best alternative approach, aligning security requirements with green supply chain principles.

Sentiment analysis of social media platforms is crucial for extracting actionable insights from unstructured textual data. However, modern sentiment analysis models using deep learning lack explainability, acting as black box and limiting trust. This study focuses on improving the explainability of sentiment analysis models of social media platforms by leveraging explainable artificial intelligence (XAI). We propose a novel explainable sentiment analysis (XSA) framework incorporating intrinsic and posthoc XAI methods, i.e., local interpretable model-agnostic explanations (LIME) and counterfactual explanations. Specifically, to solve the problem of lack of local fidelity and stability in interpretations caused by the LIME random perturbation sampling method, a new model-independent interpretation method is proposed, which uses the isometric mapping virtual sample generation method based on manifold learning instead of LIMEs random perturbation sampling method to generate samples. Additionally, a generative link tree is presented to create counterfactual explanations that maintain strong data fidelity, which constructs counterfactual narratives by leveraging examples from the training data, employing a divide-and-conquer strategy combined with local greedy. Experiments conducted on social media datasets from Twitter, YouTube comments, Yelp, and Amazon demonstrate XSAs ability to provide local aspect-level explanations while maintaining sentiment analysis performance. Analyses reveal improved model explainability and enhanced user trust, demonstrating XAIs potential in sentiment analysis of social media platforms. The proposed XSA framework provides a valuable direction for developing transparent and trust-worthy sentiment analysis models for social media platforms.

Sentiment analysis, a challenging task in understanding human emotions expressed through diverse modalities, prompts the development of innovative solutions. Multimodal data often contains important complementary information. Effective fusion and extraction of multimodal data features are key issues in sentiment analysis. In this article, we introduce a novel sentiment analysis model that integrates multimodal multiscale features based on a fuzzy-deep neural network. First, we combine multimodal data, namely text, audio, and images, to extract intrinsic feature representations. Second, our model incorporates the fuzzy-deep neural network learning module, infused with fuzzy logic principles to enhance adaptability to the inherent vagueness in sentiment expressions. Furthermore, we integrate the dual attention mechanism that dynamically focuses on pivotal aspects within multimodal data, refining feature extraction for heightened context-awareness. Rigorous validation across three datasets, including the Multimodal Corpus of Sentiment Intensity dataset, the Multimodal Opinion Sentiment and Emotion Intensity dataset, and the Chinese Single and Multimodal Sentiment dataset, demonstrates the model's superior performance in capturing the intricacies of human emotions.

Electrocardiogram (ECG) analysis and diagnosis are important auxiliary means for preventing and detecting cardiovascular diseases. Traditional approaches often face challenges due to the sheer volume of data, difficulty in extracting meaningful features, limitations in model complexity, and the requirement for real-time analysis in clinical settings. This paper presents a pioneering approach for automatic ECG diagnosis through the application of a novel Multiscale Deep Neuro-fuzzy Network (MDNFN) structure. The MDNFN is designed to address the complexity of arrhythmia classification by incorporating deep learning and fuzzy logic processing across multiscale feature extraction. To optimize the performance of the MDNFN, an innovative model optimization technique based on the Particle Swarm Optimization (PSO) algorithm is introduced, offering an efficient exploration of the parameter space. Extensive experiments across diverse datasets validate the superior performance of the proposed model compared to existing methods. The MDNFN demonstrates heightened accuracy and robustness, supported by its adaptability to different frequency and time scales inherent in ECG signals. The study establishes the model's efficacy through comprehensive experimentation, providing compelling evidence for its potential application in real-world clinical scenarios.

Medical image analysis plays a crucial role in healthcare systems of Internet of Medical Things (IoMT), aiding in the diagnosis, treatment planning, and monitoring of various diseases. With the increasing adoption of artificial intelligence (AI) techniques in medical image analysis, there is a growing need for transparency and trustworthiness in decision-making. This study explores the application of explainable AI (XAI) in the context of medical image analysis within medical cyber-physical systems (MCPS) to enhance transparency and trustworthiness. To this end, this study proposes an explainable framework that integrates machine learning and knowledge reasoning. The explainability of the model is realized when the framework evolution target feature results and reasoning results are the same and are relatively reliable. However, using these technologies also presents new challenges, including the need to ensure the security and privacy of patient data from IoMT. Therefore, attack detection is an essential aspect of MCPS security. For the MCPS model with only sensor attacks, the necessary and sufficient conditions for detecting attacks are given based on the definition of sparse observability. The corresponding attack detector and state estimator are designed by assuming that some IoMT sensors are under protection. It is expounded that the IoMT sensors under protection play an important role in improving the efficiency of attack detection and state estimation. The experimental results show that the XAI in the context of medical image analysis within MCPS improves the accuracy of lesion classification, effectively removes low-quality medical images, and realizes the explainability of recognition results. This helps doctors understand the logic of the system's decision-making and can choose whether to trust the results based on the explanation given by the framework.

The rapid evolution of transportation cyber-physical systems (T-CPS) has led to unprecedented advancements in mobility and efficiency. However, ensuring the safety and reliability of these complex systems remains a critical challenge, particularly in verifying and refining their temporal behaviors. This paper presents a novel compositional approach for temporal behavior analysis and synthesis in safety-critical T-CPS, the safety-critical temporal analysis, and refinement for the T-CPS (STAR-TCPS) framework, which combines iterative compositional verification with L*-based learning techniques to analyze and refine timing behaviors efficiently. The method leverages the clock constraint specification language for high-level timing specifications and introduces a systematic refinement process to transform abstract requirements into implementable task models. The framework incorporates innovative constraint handling mechanisms tailored for T-CPS, addressing unique challenges such as vehicle-to-infrastructure communication delays and adaptive traffic management timing. Experimental evaluations, including a case study on an intelligent vehicle system, demonstrate STAR-TCPS’s superiority over existing methods. Results show significant improvements in verification time (up to 63% reduction), memory usage (44% decrease), and task generation efficiency (18-25% fewer tasks) compared to state-of-the-art approaches. The STAR-TCPS framework enhances T-CPS’s safety and reliability by enabling more efficient verification and refinement of critical timing properties, paving the way for safer and more robust transportation systems.

Conventional security architectures and models are considered single-network architecture solutions, which assume that devices authenticated within the network are implicitly trusted. However, such an approach is unsuitable for next-generation networks (NGNs). Zero-trust security was introduced to overcome these challenges using context-aware, dynamic, and intelligent authentication schemes. This paper proposes a novel zero-trust blockchain-enabled framework for secure next-generation healthcare communication network (HCN). The proposed framework integrates zero-trust and blockchain to provide a decentralized, secure, and intelligent solution for healthcare communication in NGNs. The system model comprises three components: HCN user identity modeling, blockchain and risk assessment-based access control, and dynamic trust gateway. The user identity modeling component utilizes attribute-based user behavior trajectory features, while the access control component leverages smart contracts-based risk assessment. The dynamic trust gateway component employs a consensus mechanism to achieve dynamic gateway switching and enhance network resilience. Simulation results demonstrate that the proposed framework achieves 31% lower calculation delays, 3% higher trust values, and 3% better attack detection accuracy compared to best baseline methods. It also exhibits a 2% improvement in access control granularity and maintains 95% network throughput under various failure scenarios.

Vehicular edge computing (VEC) systems face challenges in providing real-time intelligent transportation services due to limited computing resources at VEC servers, which lead to excessive delays or denial of services, especially for latency-critical tasks. This article proposes an augmented intelligence of things (AIoT) framework to enable priority-aware task offloading in VEC for vehicle road cooperation systems, maximizing overall system rewards under latency constraints. The framework incorporates an advanced dynamic resource management mechanism that adapts to real-time data and optimizes resource allocation using augmented intelligence models. The joint priority-aware application offloading and resource optimization problem is formulated as a constrained Markov decision process, and a deep Q-network (DQN)-based learning algorithm is employed to optimize the allocation of communication and computational resources based on application priorities and real-time channel/queue state information. Simulation results demonstrate that the proposed algorithm achieves significant improvements in weighted carrying capacity, high/low-priority task drop rates, and high/low-priority task queuing delays under varying overall task arrival rates, proportions of high/low-priority tasks, vehicle density, and task size compared to benchmark schemes. The proposed AIoT-enhanced DQN-based learning algorithm advances the field of VEC systems for vehicle road cooperation, offering practical advantages, such as increased efficiency, reduced latency, and improved resource utilization, ultimately enhancing user experience and enabling real-world applications in intelligent transportation systems.

Micro-expressions, fleeting facial movements lasting 1/25 to 1/3 of a second, offer crucial insights into genuine emotions, particularly valuable in online education settings. The rapid growth of English learning livestreams has heightened the need for accurate, real-time micro-expression recognition to enhance learner engagement and instructional effectiveness. However, existing methods need help with the subtle nature of these expressions, especially in dynamic, low-resolution streaming environments. This paper presents TSG–MER–ELL, a novel end-to-end network for micro-expression recognition in English learning livestreams, integrating temporal–spatial correlation and graph attention mechanisms. The framework addresses the unique challenges of real-time emotion analysis in online language education, where subtle facial cues are crucial in understanding learner engagement and comprehension. The temporal–spatial correlation module employs action units with spatio-temporal graph convolution to aggregate features from diverse facial regions, while transformer encoders construct long-range correlations. The graph attention module builds upon local facial areas to guide self-attention computations, yielding precise local correlation features. These global and local features are fused for the final micro-expression classification. We introduce an adaptive loss function that balances accuracy, efficiency, and relevance to linguistic context. Extensive experiments on SMIC, CASME II, and SAMM datasets, adapted for English learning scenarios, demonstrate TSG–MER–ELL’s superior performance over ten state-of-the-art baselines. The TSG–MER–ELL framework achieves top UF1 and UAR scores across all datasets, significantly improving recognition speed and accuracy. Ablation studies and visualizations of temporal–spatial features and graph attention weights provide insights into the framework’s effectiveness in capturing subtle emotional cues. TSG–MER–ELL’s robust performance in varied online learning conditions highlights its potential to enhance engagement, personalize instruction, and improve overall outcomes in virtual English language education.

The proliferation of multimedia-enabled IoT devices and edge computing enables a new class of data-intensive applications. However, analyzing the massive volumes of multimedia data presents significant privacy challenges. We propose a novel framework called generative adversarial privacy (GAP) that leverages generative adversarial networks (GANs) to synthesize privacy-preserving surrogate data for multimedia analytics across the IoT-Edge continuum. GAP carefully perturbs the GAN's training process to provide rigorous differential privacy guarantees without compromising utility. Moreover, we present optimization strategies, including dynamic privacy budget allocation, adaptive gradient clipping, and weight clustering to improve convergence and data quality under a constrained privacy budget. Theoretical analysis proves that GAP provides rigorous privacy protections while enabling high-fidelity analytics. Extensive experiments on real-world multimedia datasets demonstrate that GAP outperforms existing methods, producing high-quality synthetic data for privacy-preserving multimedia processing in diverse IoT-Edge applications.

A Part-of-Speech (POS) tagger and Chunker (or shallow parser) are sequence labeling tools, crucial for improving the accuracy of Natural Language Processing (NLP) tasks like parsing, named entity recognition, sentiment analysis, information extraction, and so on. Developing such tools for a low-resource language is an arduous task. Nepali is a relatively resource-poor Indian language and has not been able to evolve from a computational perspective. Therefore, we present effective part-of-speech tagging and chunking tools for the Nepali text using sequential deep learning models—Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer (BI-LSTM-CRF) and other LSTM-based models exploring both character and word embeddings of the Nepali texts. Word Embedding has been used to capture syntactic as well as semantic information whereas character embedding has been applied to capture the morphological as well as shape information of words and also to handle the out-of-vocabulary problem. The developed chunker is the first statistical chunker for the Nepali language. A baseline model with a Conditional Random Field has also been developed to identify the optimum feature set for the aforementioned tasks. The BI-LSTM-CRF model produced an accuracy of 99.20% and 98.40%, for Nepali POS tagging and chunking, respectively. This is the highest-ever accuracy for Nepali. Thorough error analysis and observations have also been reported with examples. The developed tools can help advance research in Nepali language processing, improve the accuracy of language technology applications, and contribute to the preservation and promotion of the Nepali language.

Blockchain technology provides a secure and reliable platform for managing data in various application areas, such as supply chain management, multimedia, financial sector, food sector,Internet of Things (IoT), healthcare, and many more. The recent emergence of blockchain with IoT provides significant growth in the healthcare industry to improve security, privacy, efficiency, and transparency with more business opportunities. Nevertheless, conventional healthcare schemes suffer from various security attacks like collusion, phishing, masquerade, etc. Therefore, a privacy-preservingDistributed Application (DA)is proposed in this paper using blockchain technology to create and maintain healthcare certificates. Here, the distributed application provides an interface between the blockchain network and system objects like healthcare centers, verifiers, and regular authorities to generate and issue medical documents. In addition, it also ensures security by specifying rules using various smart contracts. To evaluate the performance of the proposed scheme, various experimental tests are conducted using the Etherscan tool for measuring operation cost, latency, and processing time. Here, the efficiency of the proposed system is also compared to the existing systems in terms of latency, throughput, and response time. The experimental results and comparative analysis show that the proposed work is more efficient than the existing techniques.

Recently, Video super-resolution (VSR) has become more crucial as the resolution of display has been grown. The majority of deep learning-based VSR methods combine the convolutional neural networks (CNN) with motion compensation or alignment module to estimate a high-resolution (HR) frame from low-resolution (LR) frames. However, most of the previous methods deal with the spatial features equally and may result in the misaligned temporal features by the pixel-based motion compensation and alignment module. It can lead to the damaging effect on the accuracy of the estimated HR feature. In this paper, we propose a wavelet attention embedding network (WAEN), including wavelet embedding network (WENet) and attention embedding network (AENet), to fully exploit the spatio-temporal informative features. The WENet is operated as a spatial feature extractor of individual low and high-frequency information based on 2-D Haar discrete wavelet transform. The meaningful temporal feature is extracted in the AENet through utilizing the weighted attention map between frames. Experimental results verify that the proposed method achieves superior performance compared with state-of-the-art methods.

This paper proposes an efficient video coding algorithm for high dynamic range (HDR) video to improve quality of compressed frame using the HEVC Main 10 profile. The proposed scheme applies an adaptive chroma QP offset based on primary of reference picture using multiple picture parameters set (PPS). The result shows improved perceptual quality with negligible bit-rate increase as compared to the anchor used in MPEG HDR/WCG Call for Evidence reference software and its chroma QP offset method.

The last few years, significant advances provide better solutions for interconnectivity and security in Intelligent Buildings (IBs). An overview of these advances is presented in this paper. Also, in this paper, we presented a patient monitoring system for hospital buildings and a Building Management System (BMS) layered design. Then, we made a comparative analysis of our system over other similar systems and presented the benefits that our approach has. Moreover, we proposed some security solutions that came out from the design and from further research. Also, a comparative analysis of our system over other similar systems is shown in this paper. Then, the benefits of our design are listed and explained. Finally, we have studied and used the Contiki OS and the Cooja emulator, and we have created simulations, which helped us have a more detailed and realistic image of our system. The results from the transmission of the packets of data are significant, since the protocol we use (CoAP), has a very low percentage, if not zero, of the packets, lost. Using the Cooja emulator we track the trafficc in and out of the network in real time. So, it seems to be a very useful tool which offers, except the traffic, diagrams and other metrics which describe our system. As future work, we are going to emulate our entire system with such tools like the Cooja emulator. Then, if the results are the expected, we will move on with the implementation of such a system. This paper will be a start point for better future decisions in designing intelligent building systems.

The last few years, significant advances which improve healthcare have been done in Internet of Things, in cloud computing, in video coding, and in mobile devices. This paper provides an overview of these advances. Also, we propose an IoT-based surveillance system for ubiquitous healthcare monitoring. The system consists of sensors, actuators, and cameras. Mesh topology was decided to be used as it provides important advantages. Moreover, the Constrained Application Protocol (CoAP) is used for the data compression and transferring, and the Scalable High-Efficiency Video Coding (SHVC) is used for the video compression and transferring. The SHVC can deliver the same video quality in half of the bit rate than the High-Efficiency Video Coding (HEVC). Furthermore, cloud services are provided, such as storage and real-time monitoring. Finally, we made a comparative analysis between our proposed system architecture and two other approaches. From this analysis, we can say that our system architecture has some benefits instead of other similar architectures. Also, we made an analysis of the bandwidth and the network throughput. The results are significant since the bandwidth required for the transmission of data and video is reduced.

To provide very high quality and/or high resolution video content under limited bandwidth conditions for transmission or storage, the high efficiency video coding (HEVC) has been recently finalised by the joint collaborative team on video coding. In this study, the authors propose a fast prediction unit (PU) decision method to reduce the computational complexity of HEVC encoder. They use an early PU decision in each coding unit‐level based on spatio‐temporal analyses and depth correlations. They also consider a classification of motion activity. Experimental results show that the encoding complexity can be reduced by up to 38% on average in the random access main profile configuration with only a small bit‐rate increment and a peak signal to noise ratio (PSNR) decrement, compared to high efficiency video coding test model (HM) 7.0 reference software.

H.264/AVC is the most widely used recent video coding standard. It provides a high encoding efficiency but it also has a high computational complexity. The block mode decision for motion estimation is the most time‐consuming procedure. A complexity reduction method for the block mode decision procedure is proposed. To reduce the complexity, all block modes are divided into several candidate block mode groups. The sum of the absolute difference (SAD) value, including the motion cost of each mode, is used as a classification feature to divide the block modes into several groups. A refinement method using a Bayesian model based on the average SAD value is also proposed. For B‐slices, a differential block mode allocation method is suggested. A different number of candidate modes are allocated for lists (list 0, list 1) based on the SAD value of each list after 16 × 16 block motion estimation. The proposed method achieves an average time‐saving for the total encoding time of 65% for IPPP and 66.01% for the hierarchical‐B structure.

In this paper, we propose a fast PU decision method to reduce encoder complexity. We use an early PU decision in each CU-level based on spatio-temporal analyses and depth correlations. We also consider a classification of motion activity. Experimental results show that the encoding complexity can be reduced by up to 37.98% on average in the random access Main profile configuration with only a small bit-rate increment and a PSNR decrement, compared to high efficiency video coding test model (HM) 7.0 reference software.

A cross-based framework strategy for performance of gradient-based weight cost aggregation strategy is presented. We formulate the process as a local regression problem consisting of two main steps. The first step is to calculate estimates for a set of points within a shape-adaptive local support region. The second step is to aggregate the matching cost for the gradient-based weight of the support region at the outmost pixel. The proposed algorithm achieves strong results in an efficient manner using the two main steps. We have achieved improvement of up to 6.9%, 8.4% and 8.3%, when compared with Adaptive Support weight (ASW) algorithm. Comparing to Cross-based algorithm, the proposed algorithm gives 2.0%, 1.3% and 1.0% in terms of non-occlusion, all and discontinuities, respectively.

Block-based motion estimation (ME) has been widely used in various video coding standards due to its effectiveness in removing temporal redundancy. However, this traditional ME technique suffers the limitation that it can only compensate for a pure parallel translation between frames. Various algorithms have been proposed for estimating other motion models, such as zooming, rotating, tilting, and warping. In spite of good performance for these algorithms, they are not used in the current industrial world because of their high computational complexity and incompatibility with current block-based video coding standards that limit their practical usage. In this paper, we present a novel zoom motion estimation (ZME) and motion compensation algorithm using the local area scaling technique. To represent and encode the zoom motion, we introduce a zoom vector (ZV) that either increases or decreases a reference block size. The resized reference block is then interpolated to the size of the current block in order to perform block matching. For fast ZME, we have also designed a 3-D diamond pattern search to reduce the number of unnecessary search points for the ZV. We have integrated the proposed algorithm into H.264/AVC JM reference software. Experimental results show that an average bit-rate savings of 11.01% (up to 16.87%) can be achieved for high-definition size sequences with an average 0.20 dB gain in peak signal-to-noise ratio and a less than 10% time increment in the hierarchical B structure.

The recent H.264/AVC video coding standard provides a higher coding efficiency than previous standards. H.264/AVC achieves a bit rate saving of more than 50 % with many new technologies, but it is computationally complex. Most of fast mode decision algorithms have focused on Baseline profile of H.264/AVC which does not consider B-picture coding. In this paper, a fast block mode decision scheme for B-pictures in High profile and Main profile is proposed to reduce the computational complexity for H.264/AVC. To reduce the block mode decision complexity in B-pictures of High profile, we use the SAD value after 16×16 block motion estimation. This SAD value is used for the classification feature to divide all block modes into some proper candidate search block modes. A differential mode allocation method is also used for the list (list 0, list 1) of B-slices based on the SAD value of the 16 × 16 block mode. The proposed algorithm shows the average speed-up factors of 41.9 ∼ 58.57% for IBBPBB sequences with a negligible bit increment and a minimal loss of image quality.

Scalable video coding (SVC) is the ongoing standard as an extension of H.264/AVC to provide stable multimedia services to various service environments and end systems. An exhaustive block motion estimation has been used to determine the best coding mode for each block type in this standard. This achieves a high coding efficiency; however it causes very high computational complexity in the encoding system. To reduce this complexity, we propose a novel algorithm for inter-frame coding based on a dynamic search range control (DSRC) mechanism to provide coarse grain signal-to-noise ratio (CGS) and spatial scalability. An efficient learning method is applied to control the search ranges of the motion estimation process according to block modes from the base layer where the full search is used. We use an adaptive learning scheme based on the steepest decent adjustment with mode information from the base layer. This scheme is also extended to the training search range in the base layer. We verify that the overall encoding time can be reduced approximately 84% based on a comparative analysis of experimental results using JSVM 9.12 reference software.

In scalable video coding (SVC) standard, an exhaustive block mode search has been employed to determine the best coding mode for each macroblock (MB). This gives a high coding efficiency, however it causes very high computational complexity in encoding system. To reduce this complexity of the exhaustive mode search, we propose a fast mode determination algorithm for inter-frame coding based on correlative information between base layer and enhancement layers for providing coarse grain signal-to-noise ratio (CGS) scalability and spatial scalability. In the proposed algorithm, we use rate-distortion (RD) costs of BL_SKIP (base layer skip) and 16 times 16 modes, and mode information of a corresponding block of the base layer to finish inter-mode search procedure early in the neighboring enhancement layer. Also, an adaptive thresholding scheme is designed. We verify that the overall encoding time for all layers can be reduced up to 65% based on comparative analysis of experimental results with JSVM 9.12 reference software.

An efficient intra mode decision scheme is proposed to reduce the computational complexity of inter frame coding in for the H.264/AVC. To decrease this computational burden, we propose an adaptive thresholding method based on distribution characteristics of the sum of the absolute differences (SAD) for the best inter mode when the intra mode is the final coding mode. We also include a simple refinement process by using spatial correlation between neighbouring marcoblocks (MBs) and the current MB. The performance of the proposed scheme is presented in terms of reduction in encoding times and PSNR values, and the increments in bit amounts.

A fast intra mode determination algorithm based on the macro-block (MB) tracking scheme and rate-distortion (RD) cost is proposed for inter-frames in the H.264/AVC video standard. In addition to the inter mode search procedure with variable block size, an intra mode search causes a significant increase in the complexity and computational load for an inter frame. To reduce the computational load of the intra mode search at the inter frame, the rate-distortion (RD) cost of the tracked MB for the current MB are used and we propose adaptive thresholding algorithms for skipping the intra mode search. For the IPPP sequence type, the overall encoding time can be reduced up to 52% through comparative analysis of experimental results with JM reference software.

A fast selective-intra mode search algorithm based on the rate-distortion (RD) cost for an inter-frame is proposed for H.264/AVC video encoding. In addition to the inter-mode search procedure with variable block size, an intra mode search causes a significant increase in the complexity and computational load for an inter-frame. To reduce the computational load of the intra mode search at the inter-frame, the RD costs of the neighborhood mac-roblocks (MBs) for the current MB are used and we propose an adaptive thresholding scheme for skipping intra mode search. For the IPPP sequence type, the overall encoding time can be reduced up to 40% and 42% for the IBBPBBP sequence type through comparative analysis of experimental results with JM reference software.

We propose a fast macroblock (MB) mode prediction and decision algorithm based on temporal correlation for P-slices in the H.264/AVC video standard. There are nine 4times4 and 8times8 modes, and four 16 times 16 modes in the intra-mode prediction and 8 block types including SKIP mode exist for the best coding gain based on rate-distortion (R-D) optimization. This scheme gives rise to exhaustive computations (search) in the coding procedure. To overcome this problem, a thresholding method for fast inter-mode decision using an MB tracking scheme to find the most correlated block and the R-D cost of that block, are suggested for early inter-mode determination. An inter-mode candidate selection method is first suggested through the statistical analysis. Then, an adaptive inter-mode search algorithm is applied by using the R-D cost of the most correlated MB. Through comparative analysis, a speed-up factor of up to 70.83 % was verified with a negligible bit increment and a minimal loss of image quality.

A fast inter-mode determination algorithm based on the macro-block (MB) tracking scheme and rate-distortion (RD) cost is proposed for the H.264/AVC video standard in which residual prediction is composed of intra-modes and inter-modes. In addition to intra-mode prediction, 8 block types exist for the best coding gain based on rate-distortion (RD) optimization in the inter-mode prediction. This scheme gives rise to exhaustive computations (search) in the coding procedure. To reduce the computational load of the inter-mode search at the inter-frame, we propose a new inter-mode determination algorithm based on the rate-distortion (RD) cost of the neighborhood MB that is tracked for the current MB in the previous frame. Based on the MB tracking scheme, an efficient sequential mode search approach is presented. We verify the performance of the proposed scheme through comparative analysis of experimental results using JM reference software.

Block motion estimation with full search is computationally complex. To reduce this complexity, different methods have been proposed, including partial distortion, which can reduce the computational complexity with no loss of image quality. We propose a distortion-based partial distortion search (DPDS) based on the magnitude of distortion and adaptive update of the matching order. We calculate absolute differences for all pixels in the predicted block point. Pixels are then sorted by the amount of distortion in a descending order for the matching process, which produces a scanning map. The sum of the absolute differences (SAD) of other candidate positions is then computed from this matching order. We also use an update of the scanning map by checking the increase in the number of absolute differences for the SAD value. The proposed DPDS algorithm improves the computational efficiency, compared with the original PDS scheme, because the accumulated value of the absolute pixel differences can rapidly reach the current minimum SAD value. The proposed algorithm is 4-13 times faster than the full search method with the same visual quality.

We propose a simple fast block-matching algorithm (BMA) based on the direction of the predicted motion vector called directional & adaptive diamond search by adaptive pattern switching (DADS-APS). The proposal method has two sequential search steps, including 1) an initial search, and 2) a refinement search for the local area. Adaptive pattern switching (APS) is proposed for the initial search and a unit-size rood pattern is used for the refinement. The initial search step consists of pattern size determination and selection of a pattern shape. We use an adaptive pattern size that is adjusted by the amount of motion. This method is superior to a fixed-pattern size algorithm, regardless of the amount of motion. In video sequences, each motion has a unique direction. using this property, we use adaptive pattern switching between ARPS and DADS. APS considers the motion direction so we can easily and correctly find the minimal matching error (MME) point with less error distortion. APS can thus reduce the number of poorly related search points. Analysis shows that DADS-APS exhibits good PSNR performance and the average number of search points compares favorably with other methods.

We propose a fast inter-mode prediction and decision algorithm based on a contextual information for the P-slices in the H.264/AVC video standard. In the H.264/AVC standard, motion estimation part is composed of intra-modes and inter-modes. In the inter-mode prediction, 7 block types exist to get the best coding gain based on rate-distortion (R-D) optimization. This scheme gives rise to an exhaustive computation (search) in the coding procedure. To overcome this computational exhaustiveness, an improved technique is suggested by using the contextual prediction. Also, we will verify the performance of the proposed scheme through the analysis of some experimental results.

In this paper, a novel profile verification scheme based on the ear, contour lines and feature points from head profile images is proposed. From the side image of a human head taken by a CCD camera, the contour line of the image is extracted by the simple filling method and morphological filtering. Based on the reference coordinate set by using ear images enrolled in the database a priori, the features of the contour line such as the tip of the nose, bottom of the nose, eye point and so on are extracted. The contour of the given image is divided into segments by utilizing the extracted feature points. The directional code of each segment is proposed and the length of the segment is computed for generating the feature vector. The verification of the given profile is performed by matching these feature vectors with those of the enrolled database. The experimental results show validity of the developed method for feature extraction and profile verification.