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A Novel Reversible Data Hiding Scheme in Compressible Encrypted Grayscale-Based images

A Novel Reversible Data Hiding Scheme in Compressible Encryp...

引用

IEEE visual communications and image processing (VCIP)

作者： Ruifeng Li Masaaki Fujiyoshi Department of Computer Science Tokyo Metropolitan University Tokyo Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In recent years, the proliferation of cloud services, particularly social media, has spurred research on encryption-then-compression (EtC) systems for secure and efficient multi-media transmission. EtC systems compress encrypted images or provide compression-resilient encryption. To address the need for embedding authentication, copyright data, or tags to images in cloud services, this paper introduces a novel data hiding scheme for the EtC system. The EtC system which this paper focuses on converts an RGB color image to the YCbCr color image, followed by horizontally joining three color component images to form a grayscale image, and encrypts this grayscale image in non-overlapping blocks using block permutation, block rotation/inversion, and brightness inversion. The proposed data hiding scheme conceals data in unencrypted images and can extract it from encrypted images by manipulating diagonal pixel values within blocks. Sorting keys, generated from intra-block pixel value characteristics, ensure correct data sequence recovery. Moreover, the proposed scheme is reversible, allowing for the removal of embedded information, and uses different pixels within the same block for dual embeddings to increase payload. Compared to conventional schemes for the EtC system, the proposed scheme leverages all three visual encryption processes and considers the RGB-to-YCbCr conversion. Performance evaluation results confirm the effectiveness of the proposed scheme.

关键词： Performance evaluation visualization image coding image color analysis visual communication Social networking (online) Color Gray-scale Encryption Sorting

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MAESR360: Masked autoencoder-based 360-degree video streaming via multi-scale feature fusion

MAESR360: Masked autoencoder-based 360-degree video streamin...

引用

IEEE visual communications and image processing (VCIP)

作者： Li Yu Zhiyu Pang Moncef Gabbouj School of Computer Science Nanjing University of Information Science & Technology Nanjing China Department of Computing Sciences Tampere University Tampere Finland

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

360-degree video streaming is becoming increasingly popular for its immersive experience. Traditional adaptive tile-based streaming methods allocate the bitrates according to view-port prediction, which effectively reduces required transmission bandwidth, but it will cause serious quality degradation when the viewport prediction is inaccurate. Thus, some researchers propose visual reconstruction and enhancement-based 360-degree video streaming framework, which can reconstructs the whole frame at very low bitrates. However, existing frameworks are built upon image-based visual reconstruction methods, which do not fully consider the characteristics of videos. In this paper, we propose a masked autoencoder-based, multi-scale optimized framework for 360-degree video streaming (MAESR360), which fully considers the temporal relevance of the video. We utilize spatio-temporal downsampling and high-ratio tube masking strategies to effectively reduce the amount of transmitted data. Additionally, we design a lightweight visual reconstruction model based on multi-scale feature fusion to recover the visual quality of video frames. The effectiveness of our proposed method is demonstrated through extensive experiments.

关键词： visualization visual communication Autoencoders Bit rate Superresolution Termination of employment Immersive experience Streaming media Reconstruction algorithms image reconstruction

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Fast Adaptive Loop Filter Algorithm Based on the Optimization of Class Merging

Fast Adaptive Loop Filter Algorithm Based on the Optimizatio...

引用

IEEE visual communications and image processing (VCIP)

作者： Chengkang Huang Leilei Huang Chang Liu Shuocheng Wang Ke Xu Yibo Fan State Key Laboratory of Integrated Chips and Systems Fudan University China Institute of Microelectronic Circuits and Systems East China Normal University China Sanechips Technology Co. Chengdu China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Adaptive loop filter (ALF) is one of the new tools adopted in the next generation video coding standard Versatile Video Coding (VVC). ALF leads to a performance improvement of 2%∼6% at the expense of high computational complexity and long processing time. Especially when encoding, ALF accounts 5%∼20% for total encoding runtime. To solve this problem, this paper proposes a fast algorithm for ALF by optimizing the process of class merging, which is an important step of rate-distortion optimization (RDO) in ALF. Experimental results indicate that compared to the VVC Test Model (VTM-22.0), this method can reduce the ALF encoding runtime on average 48.16%, 60.65%, 56.15% under all-intra, low-delay P and random access configurations, with only 0.15%, 0.31%, 0.19% increase in luma Bjontegaard Delta-Bit Rate (BD-BR).

关键词： Video coding Runtime image coding visual communication Merging Adaptive filters Rate-distortion Optimization Standards Next generation networking

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H.266/VVC Time Complexity Reduction by Learned Models and image Statistical Features

H.266/VVC Time Complexity Reduction by Learned Models and Im...

引用

IEEE visual communications and image processing (VCIP)

作者： Yel-Guan Chou Jiann-Jone Chen Dept. Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

The newest video coding standard, Versatile Video Coding (VVC), adopts a quad-tree (QT) plus multi-type tree (QTMT) block partition structure and improves the compression performance by about 30%∼50%, compared with the HEVC, at the cost of higher time complexity. To reduce VVC time complexity, we proposed to use a learned model to predict Coding Unit (CU) split modes and setup thresholds based on statistical image features to eliminate unnecessary Rate-Distortion Optimization (RDO) operations. Experiments showed that, compared with the default VVC intra-coding, the proposed method saves 46.73% of encoding time, with Bjøntegaard Delta Bit Rate (BDBR) increment of 1.16%. After retraining the learned model with a specified Quantization Parameter (QP), the time reduction rate can achieve 51.79%, and the BDBR slightly increased to 2.07%. The proposed speedup coding scheme effectively reduced the VVC time complexity to make it feasible for practical application.

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Joint-Neighborhood Product Quantization for Unsupervised Cross-Modal Retrieval

Joint-Neighborhood Product Quantization for Unsupervised Cro...

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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...

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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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