With the widespread adoption of cloud storage, time-series databases have become indispensable for managing and analyzing sequential data generated on the user side over time (i.e., time-series data), thereby alleviating the computational and storage burden on resource-constrained users. However, critical security and privacy challenges-such as query privacy leakage, data exposure, and threats to storage integrity-remain inadequately addressed by existing solutions. To this end, we propose VMPQ, an efficient protocol for privacy-preserving and verifiable multi-predicate queries over time-series databases. Specifically, we introduce a new cryptographic primitive, verifiable offline/online private information retrieval (V-OO-PIR), which supports sublinear retrieval complexity while simultaneously ensuring both query privacy and result verifiability against untrusted servers. Building on V-OO-PIR, we design a dual-layer security framework that integrates replicated secret sharing (RSS) and secure multiparty computation (MPC): (1) RSS splits time-series data into two shares stored across two non-colluding servers, ensuring data confidentiality and mitigating exposure risks, and (2) MPC performs secure multiplication directly on these shares, enabling efficient evaluation of multi-predicate queries without reconstructing the original data. As a result, VMPQ ensures query privacy by preventing servers from inferring user interests across multiple predicates, while simultaneously guaranteeing data confidentiality and the verifiability of query results. Theoretical analysis confirms the security of VMPQ against malicious adversaries. Experimental results demonstrate that VMPQ reduces query latency by up to 5× compared to the state-of-the-art solution Waldo, while also enhancing throughput and preserving high storage efficiency through optimized database encoding.

Traditional person re-identification (re-ID) methods generally rely on inter-camera person images to smooth the domain disparities between cameras. However, collecting and annotating a large number of inter-camera identities is extremely difficult and time-consuming, and this makes it hard to deploy person re-ID systems in new locations. To tackle this challenge, this paper studies the single-camera-training (SCT) setting where every person in the training set only appears in one camera. In this work, we design a novel inter-intra camera identity learning (I2CIL) framework to effectively address the SCT person re-ID. Specifically, (i) we design a Dual-Branch Identity Learning (DBIL) network consisting of inter-camera and intra-camera learning branches to learn person ID discriminative information. The former learns camera-irrelevant feature representations by constraining the distance of inter-camera negative sample pairs closer than the distance of intra-camera negative sample pairs. The latter focuses on pulling the distance of intra-camera positive sample pairs closer and pushing the distance of intra-camera negative sample pairs further, partially alleviating weak ID discrimination caused by the lack of inter-camera annotations. (ii) We design a Mixed-Sampling Joint Learning (MSJL) strategy, which is capable to capture inter- and intra-camera samples and independently accomplish the inter- and intra-camera learning tasks at the same time, avoiding the mutual interference between the two tasks. Extensive experiments on two public SCT datasets prove the superiority of the proposed approach.

Verifiable data streaming (VDS) protocols enable end users with limited storage space to continuously stream data items to an untrusted cloud server, while preserving the capacity of verifying the integrity of those retrieved data items for downstream tasks. Although there has been plenty of research around the construction of VDS, we observe that they all focus on the scenario of single-user. When deploying these VDS protocols into more common applications that involve multiple users’ data (e.g., network data monitoring and stock trends analysis), the size of the proof used to prove the integrity of retrieved data items grows linearly with the number of involved users. This would bring tremendous communication overhead, especially for lightweight users. To this end, we initiate the study of VDS protocols that are suitable for multi-user (or cross-user) setting. Specifically, we first introduce a new primitive called aggregatable chameleon vector commitment (ACVC) that allows to aggregate multiple proofs from different commitments into a single proof. Then, based on ACVC, we present a communication-efficient VDS protocol for the multi-user setting. That is, when querying data items from multiple users, the size of corresponding proof is constant and independent of the number of involved users. Theoretical analysis indicates that the proposed VDS protocol outperforms previous VDS protocols in terms of communication overhead. We also implement the proposed ACVC, and conduct extensive experiments to demonstrate its practicability.

Unsupervised person re-identification (Re-ID) aims at finding the most informative features from unlabeled person datasets. Some recent approaches adopted camera-aware strategies for model training and have thereby achieved highly promising results. However, these methods simultaneously address intra-ID discrepancies of all cameras and require independent learning under each camera, which increases the complexity of algorithm. To resolve this issue, we present a camera contrast learning framework for unsupervised person Re-ID. Our method first proposes a time-based camera contrastive learning module to facilitate model learning. At each iteration, we follow the time contrast principle to select one camera centroid as proxy of each cluster. By enforcing the samples to converge to positive proxies, the correlation between features and cameras can gradually be reduced. Moreover, we design a 3-dimensional attention module to further reduce intra-ID discrepancies caused by background shifts. By re-weighting each feature map element in a spatial-channel order, our module can exactly find identity-invariant semantic cues from regions of interest in person images, no matter how the background change. Experimental results on several popular datasets prove that our work surpasses existing unsupervised person Re-ID approaches to a remarkable extent. The source codes can be found inhttps://github.com/HongweiZhang97/CCL.

As a prevailing task in video surveillance and forensics field, person re-identification (re-ID) aims to match person images captured from non-overlapped cameras. In unconstrained scenarios, person images often suffer from the resolution mismatch problem, i.e., Cross-Resolution Person Re-ID. To overcome this problem, most existing methods restore low resolution (LR) images to high resolution (HR) by super-resolution (SR). However, they only focus on the HR feature extraction and ignore the valid information from original LR images. In this work, we explore the influence of resolutions on feature extraction and develop a novel method for cross-resolution person re-ID called Multi-Resolution Representations Joint Learning (MRJL). Our method consists of a Resolution Reconstruction Network (RRN) and a Dual Feature Fusion Network (DFFN). The RRN uses an input image to construct a HR version and a LR version with an encoder and two decoders, while the DFFN adopts a dual-branch structure to generate person representations from multi-resolution images. Comprehensive experiments on five benchmarks verify the superiority of the proposed MRJL over the relevent state-of-the-art methods.

Face regions are visual focuses in conversational video communications, thus better reconstruction quality of the regions of interest (ROI) is highly desired or necessary in the bandwidth-constrained conversational video coding. In this paper, we introduce an efficient motion based face detection method to identify face blocks in the first step, which can reduce computational complexity substantially without any loss in face detection results. Then an active contour model is applied to find face contours for more refined and compact face regions. Based on the well-located and compact face regions, facial feature priority based bit allocation is proposed for face ROI based conversational video coding. Experimental results demonstrate that the proposed face region based coding can considerably improve the coding results in the face regions, compared with two other relevant video coding schemes, in terms of objective rate-distortion performance as well as subjective visual quality.

The proposed system aims to provide useful tool kits to create more effective and interactive learning environment through the use of mobile devices. This system facilitates a flexible way in delivering the course contents and interaction among the instructor and students. It allows students to take notes or to submit answers and feedbacks during a lecture in media formats customized to the users' needs and preferences. Efficient delivery schemes are proposed for media content delivery to optimize system and network resources. Furthermore, useful statistics together with location-based analysis are provided to aid the instructor in accessing the current level of understanding or attention of the students.

This paper presents an efficient mode decision algorithm for H.263 to H.264 interframe transcoding. The proposed scheme uses a support vector machines (SVMs) approach to investigate the relationship between data extracted from H.263 decoding stage and the optimal coding mode in H.264 re-encoding process. Based on the SVM classifier, the transcoder only enables a subset of candidate modes for each macroblock in the rate-distortion-optimized mode decision in H.264. The objective is to eliminate unlikely modes in earlier stages in order to achieve computation saving. Simulation results show that the proposed method can reduce the computational complexity of interframe transcoding by up to 77% while maintaining similar rate-distortion performance.

In this letter, we present an adaptive intraframe rate-quantization (R-Q) model for H.264/AVC video coding. The proposed method aims at selecting accurate quantization parameters (QP) for intra-coded frames according to the target bit rate. By taking gradient-based frame complexity measure into consideration, the model parameters can be adaptively updated. Experimental results show that when employing our proposed R-Q model, the intraframe target bits mismatch ratio can be reduced by up to 75% as compared to the traditional Cauchy-density-based model. Hence, this is extremely useful for H.264/AVC rate control applications.

In this paper, we present an improved frame level mean absolute difference (MAD) prediction model for H.264/AVC rate control. Based on the histogram information of successive frames, the MAD of current frame can be accurately estimated. Instead of only using buffer status in the frame target bits allocation process, we also take into consideration the frame complexity which depends on the improved predicted frame MAD value. The objective of this frame complexity based bit allocation is to follow the non-stationary characteristics of video source and to provide smoother visual quality. Simulation results show that by using our proposed scheme, smaller frame target bits mismatch can be achieved. In addition, the average peak signal-to-noise ratio (PSNR) for the reconstructed video is improved by up to 0.5 dB with up to 62% reduction in picture quality variation.

In this letter, a fast partial distortion search algorithm for motion estimation is presented. The proposed method is based on the observation that when normalized partial distortion is utilized, the false rejection of impossible candidates most likely occurs within a small area adjacently located near the true motion vector. Our objective is to enhance the prediction accuracy in this small predictive search area and further save computations outside this area. Experimental results show that the proposed algorithm achieves an average 42 times speedup ratio as compared to full-search algorithm with only 0.05 dB degradation in PSNR performance.

In this paper, we present a novel intra-rate estimation method for H.264 rate control. The proposed method establishes the intra-frame rate-quantization (R-Q) estimation model by using gradient-based picture complexity measure. The proposed model aims at selecting accurate quantization parameters for intra-coded frames which can fully comply with buffer constraints. By adaptively updating this R-Q model, the performance of the model can be guaranteed regardless of the changing of video frame characteristics. Simulation results show that by using our proposed scheme, better rate control for intra-frames and frames at scene changes can be achieved. As a result, the number of skipped frames is significantly reduced thus it provides improved visual quality of the reconstructed pictures.

Variable size block motion estimation is a very important technique for video coding. The new H.264 standard employs 7 different size block types which can significantly improve the coding performance compared with the previous video coding standards. On the other hand, the computational complexity of H.264 encoder increases dramatically due to the various coding modes used. An efficient inter mode decision approach is presented. The objective is to reduce the number of candidate block types in the motion estimation while maintaining the coding efficiency. Experimental results show that the proposed method can save the computation cost by up to 42% at the same PSNR and bitrate.

The three-step search algorithm has been widely used in block matching motion estimation due to its simplicity and effectiveness. The sparsely distributed checking points pattern in the first step is very suitable for searching large motion. However, for stationary or quasistationary blocks it will easily lead the search to be trapped into a local minimum. In this paper we propose a modification on the three-step search algorithm which employs a small diamond pattern in the first step, and the unrestricted search step is used to search the center area. Experimental results show that the new efficient three-step search performs better than new three-step search in terms of MSE and requires less computation by up to 15% on average.

The three-step search algorithm has been widely used in block matching motion estimation due to its simplicity and effectiveness. The sparsely distributed checking points pattern in the first step is very suitable for searching large motion. However, for quasi-stationary blocks it will easily lead the search to be trapped into a local minimum. In this paper we propose a modification on the three-step search algorithm which employs a small diamond pattern in the first step, and the unrestricted search step is used to search the center area. Experimental results show that the proposed algorithm performs better than new three-step search in terms of MSE and requires less computation by up to 15% on average.

In this paper, a novel and efficient criterion for block matching motion estimation is presented. The proposed criterion is to enhance the conventional mean absolute difference (MAD) scheme with a new smoothness constraint on the residue block. The objective is to reduce the bit rate for encoding the residue image without any degradation of the reconstructed image quality. Simulation results show that by applying the new criterion in motion estimation, both the improvement in peak signal-to-noise ratio (PSNR) and the reduction in bit rate up to 4.3% can be achieved compared to MAD.