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Tiesong Zhao

Tiesong Zhao contributes to research discovery and scholarly infrastructure.

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Computer Vision eess.IV Multimedia Multiagent Systems

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preprint2026arXiv

Beyond Fidelity: Semantic Similarity Assessment in Low-Level Image Processing

Low-level image processing has long been evaluated mainly from the perspective of visual fidelity. However, with the rise of deep learning and generative models, processed images may preserve perceptual quality while altering semantic content, making conventional Image Quality Assessment (IQA) insufficient for semantic-level assessment. In this paper, we formalize \textit{Semantic Similarity} as a new evaluation task for low-level image processing, aimed at measuring whether semantic content is preserved after processing. We further present a structured formulation of image semantics based on semantic entities and their relations, and discuss the desired properties and constraints of a valid semantic similarity index. Based on this formulation, we propose Triplet-based Semantic Similarity Score (T3S), which models image semantics through foreground entities, background entities, and relations. T3S combines semantic entity extraction, foreground-background disentanglement, and open-world class/relation modeling. Experiments on COCO and SPA-Data show that T3S consistently outperforms existing fidelity-oriented metrics and representative semantic-level baselines, while better reflecting progressive semantic changes under diverse degradations. These results highlight the importance of semantic assessment in modern low-level vision.

preprint2026arXiv

OmniVaT: Single Domain Generalization for Multimodal Visual-Tactile Learning

Visual-tactile learning (VTL) enables embodied agents to perceive the physical world by integrating visual (VIS) and tactile (TAC) sensors. However, VTL still suffers from modality discrepancies between VIS and TAC images, as well as domain gaps caused by non-standardized tactile sensors and inconsistent data collection procedures. We formulate these challenges as a new task, termed single domain generalization for multimodal VTL (SDG-VTL). In this paper, we propose an OmniVaT framework that, for the first time, successfully addresses this task. On the one hand, OmniVaT integrates a multimodal fractional Fourier adapter (MFFA) to map VIS and TAC embeddings into a unified embedding-frequency space, thereby effectively mitigating the modality gap without multi-domain training data or careful cross-modal fusion strategies. On the other hand, it also incorporates a discrete tree generation (DTG) module that obtains diverse and reliable multimodal fractional representations through a hierarchical tree structure, thereby enhancing its adaptivity to fluctuating domain shifts in unseen domains. Extensive experiments demonstrate the superior cross-domain generalization performance of OmniVaT on the SDG-VTL task.

preprint2022arXiv

$λ$-domain VVC Rate Control Based on Game Theory

Versatile Video Coding (VVC) has set a new milestone in high-efficiency video coding. In the standard encoder, the $λ$-domain rate control is incorporated for its high accuracy and good Rate-Distortion (RD) performance. In this paper, we formulate this task as a Nash equilibrium problem that effectively bargains between multiple agents, {\it i.e.}, Coding Tree Units (CTUs) in the frame. After that, we calculate the optimal $λ$ value with a two-step strategy: a Newton method to iteratively obtain an intermediate variable, and a solution of Nash equilibrium to obtain the optimal $λ$. Finally, we propose an effective CTU-level rate allocation with the optimal $λ$ value. To the best of our knowledge, we are the first to combine game theory with $λ$-domain rate control. Experimental results with Common Test Conditions (CTC) demonstrate the efficiency of the proposed method, which outperforms the state-of-the-art CTU-level rate allocation algorithms.

preprint2022arXiv

Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study

In the video coding process, the perceived quality of a compressed video is evaluated by full-reference quality evaluation metrics. However, it is difficult to obtain reference videos with perfect quality. To solve this problem, it is critical to design no-reference compressed video quality assessment algorithms, which assists in measuring the quality of experience on the server side and resource allocation on the network side. Convolutional Neural Network (CNN) has shown its advantage in Video Quality Assessment (VQA) with promising successes in recent years. A large-scale quality database is very important for learning accurate and powerful compressed video quality metrics. In this work, a semi-automatic labeling method is adopted to build a large-scale compressed video quality database, which allows us to label a large number of compressed videos with manageable human workload. The resulting Compressed Video quality database with Semi-Automatic Ratings (CVSAR), so far the largest of compressed video quality database. We train a no-reference compressed video quality assessment model with a 3D CNN for SpatioTemporal Feature Extraction and Evaluation (STFEE). Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The CVSAR database and STFEE model will be made publicly available to facilitate reproducible research.

preprint2022arXiv

Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation

The ever-growing multimedia traffic has underscored the importance of effective multimedia codecs. Among them, the up-to-date lossy video coding standard, Versatile Video Coding (VVC), has been attracting attentions of video coding community. However, the gain of VVC is achieved at the cost of significant encoding complexity, which brings the need to realize fast encoder with comparable Rate Distortion (RD) performance. In this paper, we propose to optimize the VVC complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation. At the first stage, we employ the deep convolutional network to extract the spatialtemporal neighboring coding features. Then we fuse all reference features obtained by different convolutional kernels to determine an optimal intra coding depth. At the second stage, we employ a probability-based model and the spatial-temporal coherence to select the candidate partition modes within the optimal coding depth. Finally, these selected depths and partitions are executed whilst unnecessary computations are excluded. Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences.

preprint2022arXiv

Learning-Based Video Coding with Joint Deep Compression and Enhancement

The end-to-end learning-based video compression has attracted substantial attentions by paving another way to compress video signals as stacked visual features. This paper proposes an efficient end-to-end deep video codec with jointly optimized compression and enhancement modules (JCEVC). First, we propose a dual-path generative adversarial network (DPEG) to reconstruct video details after compression. An $α$-path facilitates the structure information reconstruction with a large receptive field and multi-frame references, while a $β$-path facilitates the reconstruction of local textures. Both paths are fused and co-trained within a generative-adversarial process. Second, we reuse the DPEG network in both motion compensation and quality enhancement modules, which are further combined with other necessary modules to formulate our JCEVC framework. Third, we employ a joint training of deep video compression and enhancement that further improves the rate-distortion (RD) performance of compression. Compared with x265 LDP very fast mode, our JCEVC reduces the average bit-per-pixel (bpp) by 39.39\%/54.92\% at the same PSNR/MS-SSIM, which outperforms the state-of-the-art deep video codecs by a considerable margin.

preprint2022arXiv

SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution

The image Super-Resolution (SR) technique has greatly improved the visual quality of images by enhancing their resolutions. It also calls for an efficient SR Image Quality Assessment (SR-IQA) to evaluate those algorithms or their generated images. In this paper, we focus on the SR-IQA under deep learning and propose a Structure-and-Perception-based Quality Evaluation (SPQE). In emerging deep-learning-based SR, a generated high-quality, visually pleasing image may have different structures from its corresponding low-quality image. In such case, how to balance the quality scores between no-reference perceptual quality and referenced structural similarity is a critical issue. To help ease this problem, we give a theoretical analysis on this tradeoff and further calculate adaptive weights for the two types of quality scores. We also propose two deep-learning-based regressors to model the no-reference and referenced scores. By combining the quality scores and their weights, we propose a unified SPQE metric for SR-IQA. Experimental results demonstrate that the proposed method outperforms the state-of-the-arts in different datasets.

preprint2022arXiv

SSIM-Variation-Based Complexity Optimization for Versatile Video Coding

To date, Versatile Video Coding (VVC) has a more magnificent overall performance than High Efficiency Video Coding (HEVC). The Quadtree with Nested Multi-Type Tree (QTMT) coding block structure can substantially enhance video coding quality in VVC. However, the coding gain also leads to a greater coding complexity. Therefore, this letter proposes a Fast Decision Scheme Based on Structural Similarity Index Metric Variation (FDS-SSIMV) to solve this problem. Firstly, the Structural Similarity Index Metric Variation (SSIMV) characteristic among the sub coding units of the spit mode is illustrated. Next, to evaluate the SSIMV value, SSIMV measure strategies are designed for different split modes in this letter. Then, the desired split modes are selected by the SSIMV values. Experimental results show that the proposed method achieves 64.74\% average encoding Time Saving (TS) with a 2.79\% Bj$\varnothing$ntegaard Delta Bit Rate (BDBR), outperforming the benchmarks.

preprint2022arXiv

Timestamp-independent Haptic-Visual Synchronization

The booming haptic data significantly improves the users'immersion during multimedia interaction. As a result, the study of Haptic,Audio-Visual Environment(HAVE)has attracted attentions of multimedia community. To realize such a system, a challenging tack is the synchronization of multiple sensorial signals that is critical to user experience. Despite of audio-visual synchronization efforts, there is still a lack of haptic-aware multimedia synchronization model. In this work, we propose a timestamp-independent synchronization for haptic-visual signal transmission. First, we exploit the sequential correlations during delivery and playback of a haptic-visual communication system. Second, we develop a key sample extraction of haptic signals based on the force feedback characteristics, and a key frame extraction of visual signals based on deep object detection. Third, we combine the key samples and frames to synchronize the corresponding haptic-visual signals. Without timestamps in signal flow, the proposed method is still effective and more robust to complicated network conditions. Subjective evaluation also shows a significant improvement of user experience with the proposed method.

preprint2022arXiv

Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning

The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience; while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision.

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