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检索条件"任意字段=Conference on Visual Communications and Image Processing"

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Joint-Neighborhood Product Quantization for Unsupervised Cross-Modal Retrieval

Joint-Neighborhood Product Quantization for Unsupervised Cro...

引用

IEEE visual communications and image processing (VCIP)

作者： Runhao Li Zhenyu Weng Yongming Chen Huiping Zhuang Yap-Peng Tan Zhiping Lin School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Product quantization (PQ) is a technique that transforms high-dimensional data into compact binary codes to reduce data storage and improve search efficiency. However, existing PQ methods separate the learning of modality-specific features from the learning of quantization codewords, resulting in suboptimal performance in cross-modal retrieval tasks. In this paper, we propose a joint-neighborhood product quantization (JNPQ) method to simultaneously learn modality-specific features and quantization codewords. To achieve this, we first introduce a cross-modal quantization contrastive learning module that preserves the inter-modal neighborhood of the original data and reduces the quantization error. Then, we design a self-neighbor contrastive learning module that enhances the intra-modal neighborhood within individual modalities. Extensive experiments demonstrate that JNPQ achieves state-of-the-art results in crossmodal retrieval when compared with other unsupervised crossmodal quantization methods.

关键词： Quantization (signal) visual communication image processing Memory Contrastive learning Transforms Internet Multimedia communication Online services Cross modal retrieval

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Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment

Differentiable Low-computation Global Correlation Loss for M...

引用

IEEE visual communications and image processing (VCIP)

作者： Yipeng Liu Qi Yang Yiling Xu Cooperative MediaNet Innovation Center Shanghai Jiao Tong University Shanghai China School of Science and Engineering University of Missouri - Kansas City Kansas USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In this paper, we propose a global monotonicity consistency training strategy for quality assessment, which includes a differentiable, low-computation monotonicity evaluation loss function and a global perception training mechanism. Specifically, unlike conventional ranking loss and linear programming approaches that indirectly implement the Spearman rank-order correlation coefficient (SROCC) function, our method directly converts SROCC into a loss function by making the sorting operation within SROCC differentiable and functional. Furthermore, to mitigate the discrepancies between batch optimization during network training and global evaluation of SROCC, we introduce a memory bank mechanism. This mechanism stores gradient-free predicted results from previous batches and uses them in the current batch’s training to prevent abrupt gradient changes. We evaluate the performance of the proposed method on both images and point clouds quality assessment tasks, demonstrating performance gains in both cases.

关键词： Training Point cloud compression Correlation coefficient visual communication image processing Performance gain Linear programming Quality assessment Optimization Sorting

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Multi-Class image Generation from EEG Features with Conditional Generative Adversarial Networks

Multi-Class Image Generation from EEG Features with Conditio...

引用

International conference on Wireless communications and Signal processing (WCSP)

作者： Yiping Duan Shuzhan Hu Xin Ma Xiaoming Tao Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Beijing China

Exploring the visual coding mechanism of the human brain has been an important topic in brain-computer interface technology, holding the potential to provide new insights into the research of the Internet of Things (IoT). Especially, the use of brain signals for image generation has attracted people’s attention. In this paper, we propose a multi-class image generation method based on electroencephalography (EEG) features with conditional generative adversarial networks (CGAN). Specifically, we synchronously collect the EEG signals when subjects watch visual stimuli through EEG equipment and construct a multi-category image-EEG dataset. The obtained EEG dataset undergoes preprocessing to eliminate interference from electrooculographic signals and electromyographic signals. Based on this dataset, we extract EEG features related to visual stimuli by deep learning algorithms and integrate them with CGAN for image generation. We employ different EEG feature extraction methods for image generation and conduct comparative experiments. The experimental results demonstrate that the proposed method based on the CGAN model utilizing the joint EEG spatio-temporal and temporal-frequency features can achieve better performance in the image generation task.

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Characterizing the Geometric Complexity of G-PCC Compressed Point Clouds

Characterizing the Geometric Complexity of G-PCC Compressed ...

引用

IEEE visual communications and image processing (VCIP)

作者： Annalisa Gallina Hadi Amirpour Sara Baldoni Giuseppe Valenzise Federica Battisti Department of Information Engineering University of Padova Italy Christian Doppler Laboratory ATHENA Alpen-Adria-Universitat Klagenfurt Austria CNRS CentraleSupélec L2S (UMR 8506) Université Paris-Saclay Gif-sur-Yvette France

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Measuring the complexity of visual content is crucial in various applications, such as selecting sources to test processing algorithms, designing subjective studies, and efficiently determining the appropriate encoding parameters and bandwidth allocation for streaming. While spatial and temporal complexity measures exist for 2D videos, a geometric complexity measure for 3D content is still lacking. In this paper, we present the first study to characterize the geometric complexity of 3D point clouds. Inspired by existing complexity measures, we propose several compression-based definitions of geometric complexity derived from the rate-distortion curves obtained by compressing a dataset of point clouds using G-PCC. Additionally, we introduce density-based and geometry-based descriptors to predict complexity. Our initial results show that even simple density measures can accurately predict the geometric complexity of point clouds.

关键词： Point cloud compression visualization image coding Three-dimensional displays visual communication Density measurement Rate-distortion Channel allocation Complexity theory Videos

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Encoder Optimization Information SEI Message for Identifying Optimization Objectives and Methods

Encoder Optimization Information SEI Message for Identifying...

引用

IEEE visual communications and image processing (VCIP)

作者： Chul Keun Kim Hendry Tan Jaehyun Lim Seung Hwan Kim LG Electronics Seoul Korea LG Electronics San Diego USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

This paper presents the Encoder Optimization Information (EOI) SEI message, which was recently included in the Versatile Supplemental Enhancement Information (VSEI) version 4 draft standard and is ongoing work within the Joint Video Experts Team (JVET) group. As the use of video content diversifies, there is an increasing need to apply preprocessing/task-driven encoding to optimize the coded bitstream for its intended use. Such optimization may be targeted for human viewing, machine analysis tasks, or both. Additionally, for better utilization of the coded bitstream, tailoring optimization for intended uses allows for the exclusion of unnecessary information, potentially reducing bitrate and enhancing compression efficiency. The EOI SEI message is designed to contain information about the optimization applied during the video encoding process and the intended usages of such optimization. Decoders may utilize the information provided by the EOI SEI message to effectively use video content that best fits their specific requirements and conditions.

关键词： image coding visual communication Bit rate Decoding Object recognition Optimization Standards Binary sequences

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Efficient Lightweight Attention Based Learned image Compression

Efficient Lightweight Attention Based Learned Image Compress...

引用

IEEE visual communications and image processing (VCIP)

作者： Ziyang He Lei Luo Le Zhang Hongwei Guo Ce Zhu School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Engineering Honghe University Yunnan China

The CNN-based end-to-end learned image compression methods have already achieved a significant improvement in terms of coding efficiency. Moreover, with the capability of modeling long-range global correlation, the transformer-based image compression has further elevated the coding efficiency to outperform the latest Versatile Video Coding (VVC) standard. Nonetheless, the high computational burden of the self-attention mechanism in the transformer design present a significant obstacle for practical applications. To address this concern, we propose a relatively low-complexity end-to-end learned image compression approach by integrating an efficient lightweight attention module, which effectively mitigates the computational overhead associated with self-attention in the transformer design. The experimental results demonstrate that the proposed method achieves better RD performance than VVC. Furthermore, as compared to the prevailing state-of-the-art transformer-based approach, our method accelerates the coding speed by over 6 times while maintaining comparable coding efficiency.

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Vectorized Angular Intra Prediction for Practical VVC Encoding

Vectorized Angular Intra Prediction for Practical VVC Encodi...

引用

IEEE visual communications and image processing (VCIP)

作者： Kari Siivonen Joose Sainio Guillaume Gautier Alexandre Mercat Jarno Vanne Ultra Video Group Tampere University Finland

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Versatile Video Coding (VVC) provides new coding tools for more efficient intra prediction but with a substantial increase in computational complexity. This paper introduces vectorized kernels for 8-bit angular intra prediction and position dependent intra prediction combination (PDPC), which are carefully optimized for all block sizes and prediction modes of VVC. The proposed kernels streamline the filtering process and utilize optimized memory access patterns. Our standalone tests show that the proposed vectorization achieves speedups of 6.68× for luma and 4.40× for chroma predictions over scalar implementations. Integrating these kernels into the practical uvg266 VVC encoder provides speedups of 1.07× in the slowest configuration and 1.68× in the fastest configuration. The reported speedups are obtained without any coding overhead, so the proposed vectorization plays an integral role in pursuing real-time VVC coding with high coding efficiency.

关键词： Video coding image coding visual communication Filtering Real-time systems Encoding Kernel Computational complexity

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LLaVA-Tour: A Large Multimodal Model for Japanese Tourist Spot Prediction and Review Generation

LLaVA-Tour: A Large Multimodal Model for Japanese Tourist Sp...

引用

IEEE visual communications and image processing (VCIP)

作者： Hiromasa Yamanishi Ling Xiao Toshihiko Yamasaki The University of Tokyo

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Tourist landmark recognition and review generation can significantly enhance travel planning by helping travelers make informed decisions, optimize experiences, and discover new destinations. These capabilities also benefit local economies and improve business efficiency in tourism. The advancements in large multimodal models have demonstrated high performance across a wide range of image processing tasks due to their extensive knowledge and reasoning capabilities. However, there are no large-scale tourism datasets and no models specifically tailored for tourism have been developed. To address these issues, we created a new dataset with over 1.3 million entries from Japanese tourism website ***, covering three types of tasks: landmark recognition, general and conditioned review generation and one support task: description generation. Using the Large Language-and-Vision Assistant (LLaVA) as the baseline, we developed instruction tuning strategies for these tasks and created a new Large Multimodal Model, LLaVA-Tour. By integrating domain-specific knowledge, our model outperformed state-of-the-art large multimodal models and review generation models in landmark recognition, and general and conditioned review generation. The code and data url are available at https://***/HiromasaYamanishi/LLaVATour.

关键词： image recognition Codes Reviews visual communication Biological system modeling Predictive models Question answering (information retrieval) Cognition Planning Tuning

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Effect of image Noise Removal Using Bottleneck Attention Mechanism

Effect of Image Noise Removal Using Bottleneck Attention Mec...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Neval Karaca Serdar Çiftci Bilgisayar Mühendisliği Bölümü Harran Üniversitesi Şanlıurfa Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

In computer vision applications, image enhancement is important for improving image quality and extracting meaningful information. Noise removal is a commonly used technique in image enhancement. In this study, the Batch Renormalization Denoising Network (BRDNet), which performs well in noise removal, is used as the base model with the use of the Bottleneck Attention Module (BAM) to achieve performance improvement. The proposed method is tested on different datasets with different noise levels and their results are compared. In quantitative experiments, an increase in the PSNR metric value was observed and the visual results were found to be closer to the target images.

关键词： Measurement image quality visualization Computer vision Computational modeling Noise Noise reduction

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Proposal-level Correction Guided by CLIP for Few-shot Object Detection

Proposal-level Correction Guided by CLIP for Few-shot Object...

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IEEE visual communications and image processing (VCIP)

作者： Ruihang Wang Taijin Zhao Hefei Mei Heqian Qiu Lanxiao Wang Hongliang Li University of Electronic Science and Technology of China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Few-shot object detection aims at detecting previously unseen objects given only a few annotated samples. Most existing approaches treat the model obtained from the base training stage with abundant data as a container of prior knowledge that can be transferred to novel objects. Knowledge with similar properties is also contained in Contrastive Language-image Pretraining (CLIP). In this paper, we utilize this external prior knowledge to generate proposal-level classification scores to improve the detection results. We notice that these scores can hardly reflect the quality of proposal localization, so we combine them with the ones from a conventional detector to obtain the ability to distinguish the background. Moreover, we propose a new score fusion module with regularization to alleviate the ambiguity of detection results generated by a trivial element-wise multiplication fusion method. To further improve the quality of classification scores in our proposed branch, we add learnable prompts to mitigate the inaccurate classification problem we observe. We conduct extensive experiments on the PASCAL VOC dataset and demonstrate the effectiveness of our approach.

关键词： Training Location awareness visual communication image processing Object detection Detectors Containers Data models Proposals

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