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VisCode: Embedding Information in Visualization images using Encoder-Decoder Network

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021年第2期27卷 326-336页

作者： Zhang, Peiying Li, Chenhui Wang, Changbo East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China

We present an approach called VisCode for embedding information into visualization images. This technology can implicitly embed data information specified by the user into a visualization while ensuring that the encoded visualization image is not distorted. The VisCode framework is based on a deep neural network. We propose to use visualization images and QR codes data as training data and design a robust deep encoder-decoder network. The designed model considers the salient features of visualization images to reduce the explicit visual loss caused by encoding. To further support large-scale encoding and decoding, we consider the characteristics of information visualization and propose a saliency-based QR code layout algorithm. We present a variety of practical applications of VisCode in the context of information visualization and conduct a comprehensive evaluation of the perceptual quality of encoding, decoding success rate, anti-attack capability, time performance, etc. The evaluation results demonstrate the effectiveness of VisCode.

关键词： Data visualization Visualization Decoding image coding Encoding image color analysis Media Information visualization information steganography autocoding saliency detection visualization retargeting

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Deep Collaborative Discrete Hashing With Semantic-Invariant Structure Construction

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IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 1274-1286页

作者： Wang, Zijian Zhang, Zheng Luo, Yandan Huang, Zi Shen, Heng Tao Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Harbin Inst Technol Biocomp Res Ctr Shenzhen 518055 Peoples R China Harbin Inst Technol Shenzhen Key Lab Visual Object Detect & Recognit Shenzhen 518055 Peoples R China Univ Elect Sci & Technol China Ctr Future Media Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

While deep hashing has made great progress in large-scale multimedia retrieval, most of the existing approaches under-explore the semantic correlations and neglect the effect of context-aware visual learning. In this paper, we propose a dual-stream learning framework, termed as Deep Collaborative Discrete Hashing (DCDH), which constructs a discriminative common discrete space by collaboratively incorporating the shared and individual semantics deduced from visual features and semantics. Specifically, DCDH generates context-aware representations by employing the outer product of visual embeddings and semantic encodings. To further preserve the original semantics and alleviate the class imbalance problem, we introduce the focal loss to take advantage of frequent and rare concepts. Furthermore, a common binary code space is constructed based on the joint learning of the visual representations, the context-aware representations, and the label distribution calibration. Three losses, i.e., the pairwise similarity loss, the quantization loss, and the balanced classification loss, are collaboratively optimized in the general learning framework of DCDH. Extensive experiments conducted on three large-scale benchmark datasets demonstrate the superiority of the proposed method, yielding the state-of-the-art image retrieval performance.

关键词： Semantics Visualization Binary codes Quantization (signal) image coding Collaboration Correlation Collaborative hashing semantic encoding image retrieval discriminative learning

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Compressed Pseudo-Analog Transmission System for Remote Sensing images Over Bandwidth-Constrained Wireless Channels

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第9期30卷 3181-3195页

作者： Gui, Yongqiang Lu, Hancheng Jiang, Xiaoda Wu, Feng Chen, Chang Wen Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China SUNY Buffalo Univ Buffalo Dept Comp Sci & Engn Buffalo NY 14228 USA

Recently, pseudo-analog transmission based on SoftCast has been proposed to improve the received quality of video/image by eliminating the cliff effect in traditional digital transmission. In this paper, we propose a Compressed Pseudo-analog Transmission System (ComPaTS) for remote sensing images over bandwidth-constrained wireless channels. This novel scheme is developed based on the observation that the inherent dropping strategy in pseudo-analog transmission is impractical for remote sensing images in which the transmission bandwidth is generally insufficient. In ComPaTS, to guarantee the content diversity gain under pseudo-analog transmission, block-based Compressive Sensing (CS) is applied to the wavelet domain of each remote sensing image, where the sampling ratio is proportional to the importance of different blocks. The main work of ComPaTS is to leverage the sampling ratio in block-based CS and the resource allocation in pseudo-analog transmission in order to minimize system distortion. Two components of system distortion, i.e., source distortion and channel distortion are analyzed respectively. To characterize the coupling relationship between these two different types of distortion, a joint bandwidth-power distortion optimization problem is formulated. Furthermore, we also propose a two-stage allocation algorithm to solve the problem efficiently. The simulation results demonstrate that the proposed ComPaTS scheme significantly outperforms reference schemes in terms of peak signal-to-noise ratio under different bandwidth-constrained scenarios.

关键词： Bandwidth Remote sensing Distortion Resource management image coding Wireless sensor networks Degradation Pseudo-analog transmission compressive sensing resource allocation remote sensing image bandwidth-constrained wireless channels

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Blockchain-Watermarking for Compressive Sensed images

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IEEE ACCESS 2021年 9卷 56457-56467页

作者： Li, Ming Zeng, Leilei Zhao, Le Yang, Renlin An, Dezhi Fan, Haiju Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Henan Normal Univ Engn Lab Intelligence Business & Internet Things Xinxiang 453007 Henan Peoples R China Henan Normal Univ Big Data Engn Lab Teaching Resources & Assessment Xinxiang 453007 Henan Peoples R China Key Lab Artificial Intelligence & Personalized Le Xinxiang 453007 Henan Peoples R China Gansu Univ Polit Sci & Law Sch Cyber Secur Lanzhou 730070 Peoples R China

With the application of multimedia big data, the problems such as information leakage and data tampering have emerged. The security of images which is one of the most typical multimedia has become a major problem facing the large-scale open network environment. This paper proposed a blockchain-watermarking scheme to protect the privacy, integrity and availability of compressed sensed images, which effectively combines multimedia watermarking, compressed sensing, Interplanetary File System (IPFS) and blockchain technologies. Based on the reliable authentication of watermarking, the confidentiality protection of compressed sensing, the secure storage of IPFS, and the decentralization and non-tamperability of blockchain, the all-round security protection of the image big data based on compressive sensing can be realized. Experiments show that the proposed scheme is effective and feasible.

关键词： Watermarking Blockchain Compressed sensing Big Data image coding Privacy Peer-to-peer computing Blockchain IPFS compressed sensing watermarking data hiding

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WebP-JPEG Transcoding Detection by Spotting Re-Compression Artifacts With CNN-ViT for Processing Dual-Domain Features

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第12期34卷 12535-12549页

作者： Li, Bin Shi, Jian Li, Weixiang Li, Haodong Shenzhen Univ Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China Shenzhen Univ SZU AFS Joint Innovat Ctr AI Technol Shenzhen 518060 Peoples R China

The trace of double compression can serve as a crucial evidence of image manipulation for forensic investigation. With the ever-increasing popularity of WebP format, a new type of double compression case, WebP-JPEG transcoding, has emerged. However, distinguishing it from two common compression cases, single JPEG (SJPEG) and double JPEG (DJPEG) has not yet been studied. In this paper, we propose a specialized method for the new task. Firstly, a detailed analysis is conducted to reveal the differences in compression artifacts between WebP-JPEG and SJPEG/DJPEG, which manifests in the distributions of $4\times 4$ / $8\times 8$ DCT coefficients and the high-frequency portions of image spectrum. Then, multi-modality DCT histograms (MMDH) and high-pass-filtered image residuals (HPFIR) are proposed as front-end dual-domain forensic features to expose the above differences. An indispensable part of these features are extracted through a novel frequency-isolation module (FIM), offering additional information based on the derived relationship between $4\times 4$ and $8\times 8$ DCT coefficients. Finally, a CNN-ViT (Convolutional Neural Network-Vision Transformer) dual-stream network is designed to learn back-end deep features for a reliable detection, where a CNN stream is used to process statistical features in MMDH while a ViT stream to learn spatial correlations in HPFIR. Extensive experimental results demonstrate that the proposed method significantly outperforms state-of-the-art double compression detection methods in distinguishing WebP-JPEG from SJPEG/DJPEG and is more effective in tampering localization. In specific, the proposed method achieves an average detection accuracy of 0.942 for small images of size $128\times 128$ .

关键词： Discrete cosine transforms Transform coding image coding Quantization (signal) Transcoding Forensics Feature extraction image forensics compression history WebP images JPEG images re-compression artifacts

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Sparse overcomplete Gabor wavelet representation based on local competitions

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IEEE TRANSACTIONS ON image PROCESSING 2006年第2期15卷 265-272页

作者： Fischer, S Cristóbal, G Redondo, R CSIC Inst Opt E-28006 Madrid Spain

Gabor representations present a number of interesting properties despite the fact that the basis functions are nonorthogonal and provide an overcomplete representation or a nonexact reconstruction. Overcompleteness involves an expansion of the number of coefficients in the transform domain and induces a redundancy that can be further reduced through computational costly iterative algorithms like Matching Pursuit. Here, a biologically plausible algorithm based on competitions between neighboring coefficients is employed for adaptively representing any source image by a selected subset of Gabor functions. This scheme involves a sharper edge localization and a significant reduction of the information redundancy, while, at the same time, the reconstruction quality is preserved. The method is characterized by its biological plausibility and promising results, but it still requires a more in depth theoretical analysis for completing its validation.

关键词： biological system modeling image coding image edge analysis overcomplete representations wavelet transforms

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An Improved Medical image Compression Method Based on Wavelet Difference Reduction

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IEEE ACCESS 2023年 11卷 18026-18037页

作者： Zerva, Matina C. H. Christou, Vasileios Giannakeas, Nikolaos Tzallas, Alexandros T. Kondi, Lisimachos P. Univ Ioannina Dept Comp Sci & Engn Ioannina 45110 Greece Univ Ioannina Dept Informat & Telecommun Arta 47100 Greece

Advanced microscopic techniques such as high-throughput, high-content, multispectral, and 3D imaging could include many images per experiment requiring hundreds of gigabytes (GBs) of memory. Efficient lossy image-compression methods such as joint photographic experts group (JPEG) and JPEG 2000 are crucial to managing these large amounts of data. However, these methods can get visual quality with high compression ratios but do not necessarily maintain the medical data and information integrity. This paper proposes a novel and improved medical image compression method based on color wavelet difference reduction. Specifically, the proposed method is an extension of the standard wavelet difference reduction (WDR) method using mean co-located pixel difference to select the optimum quantity of color images that present the highest similarity in the spatial and temporal domain. The images with large spatiotemporal coherence are encoded as one volume and evaluated regarding the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed method is evaluated in the challenging histopathological microscopy image analysis field using 31 slides of colorectal cancer. It is found that the perceptual quality of the medical image is remarkably high. The results indicate that the PSNR improvement over existing schemes may reach up to 22.65 dB compared to JPEG 2000. Also, it can reach up to 10.33dB compared to a method utilizing discrete wavelet transform (DWT), leading us to implement a mobile and web platform that can be used for compressing and transmitting microscopic medical images in real time.

关键词： image coding Biomedical imaging Medical diagnostic imaging Transform coding Three-dimensional displays Microscopy image color analysis Imaging microscopy image compression color wavelet difference reduction

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Fine-Grained Video Retrieval With Scene Sketches

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IEEE TRANSACTIONS ON image PROCESSING 2023年 32卷 3136-3149页

作者： Zuo, Ran Deng, Xiaoming Chen, Keqi Zhang, Zhengming Lai, Yu-Kun Liu, Fang Ma, Cuixia Wang, Hao Liu, Yong-Jin Wang, Hongan Chinese Acad Sci Inst Software Beijing Key Lab Human Comp Interact Beijing 100190 Peoples R China Univ Chinese Acad Sci Dept Comp Sci & Technol Beijing 101408 Peoples R China Cardiff Univ Dept Comp Sci & Informat Cardiff CF24 4AG Wales Tsinghua Univ BNRist Dept Comp Sci & Technol Beijing 100084 Peoples R China Alibaba Beijing 100102 Peoples R China

Benefiting from the intuitiveness and naturalness of sketch interaction, sketch-based video retrieval (SBVR) has received considerable attention in the video retrieval research area. However, most existing SBVR research still lacks the capability of accurate video retrieval with fine-grained scene content. To address this problem, in this paper we investigate a new task, which focuses on retrieving the target video by utilizing a fine-grained storyboard sketch depicting the scene layout and major foreground instances' visual characteristics (e.g., appearance, size, pose, etc.) of video;we call such a task "fine-grained scene-level SBVR". The most challenging issue in this task is how to perform scene-level cross-modal alignment between sketch and video. Our solution consists of two parts. First, we construct a scene-level sketch-video dataset called SketchVideo, in which sketch-video pairs are provided and each pair contains a clip-level storyboard sketch and several keyframe sketches (corresponding to video frames). Second, we propose a novel deep learning architecture called Sketch Query Graph Convolutional Network (SQ-GCN). In SQ-GCN, we first adaptively sample the video frames to improve video encoding efficiency, and then construct appearance and category graphs to jointly model visual and semantic alignment between sketch and video. Experiments show that our fine-grained scene-level SBVR framework with SQ-GCN architecture outperforms the state-of-the-art fine-grained retrieval methods. The SketchVideo dataset and SQ-GCN code are available in the project webpage https://***/FG-SL-SBVR/.

关键词： Task analysis Semantics Visualization Convolutional neural networks Layout image coding Encoding Fine-grained sketch-based video retrieval sketch-video dataset scene sketch graph convolutional networks

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The application of multiwavelet filterbanks to image processing

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IEEE TRANSACTIONS ON image PROCESSING 1999年第4期8卷 548-563页

作者： Strela, V Heller, PN Strang, G Topiwala, P Heil, C Dartmouth Coll Dept Math Hanover NH 03755 USA Aware Inc Bedford MA 01730 USA MIT Dept Math Cambridge MA 02139 USA Univ So Calif Inst Informat Sci Arlington VA 22203 USA Georgia Inst Technol Dept Math Atlanta GA 30332 USA

Multiwavelets are a new addition to the body of wavelet theory. Realizable as matrix-valued filterbanks leading to wavelet bases, multi wavelets offer simultaneous orthogonality, symmetry, and short support, which is not possible with scalar two-channel wavelet systems. After reviewing this recently developed theory, we examine the use of multiwavelets in a filterbank setting for discrete-time signal and image processing, Multiwavelets differ from scalar wavelet systems in requiring two or more input streams to the multiwavelet filterbank, We describe two methods (repeated row and approximation/deapproximation) for obtaining such a vector input stream from a one-dimensional (1-D) signal, Algorithms for symmetric extension of signals at boundaries are then developed, and naturally integrated with approximation-based preprocessing, We describe an additional algorithm for multiwavelet processing of two-dimensional (2-D) signals, two rows at a time, and develop a new family of multiwavelets (the constrained pairs) that is well-suited to this approach. This suite of novel techniques is then applied to two Basic signal processing problems, denoising via wavelet-shrinkage, and data compression, After developing the approach via model problems in one dimension, we apply multiwavelet processing to images, frequently obtaining performance superior to the comparable scalar wavelet transform.

关键词： denoising filterbanks image coding multiwavelets wavelets

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Scalar vector quantization of medical images

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IEEE TRANSACTIONS ON image PROCESSING 1996年第2期5卷 387-392页

作者： Mohsenian, N Shahri, H Nasrabadi, NM AT&T BELL LABS MURRAY HILLNJ 07974 SUNY BUFFALO DEPT ELECT & COMP ENGNBUFFALONY 14260

A new coding scheme based on the scalar-vector quantizer (SVQ) is developed for compression of medical images. SVQ is a fixed-rate encoder and its rate-distortion performance is close to that of optimal entropy-constrained scalar quantizers (ECSQ's) for memoryless sources, The use of a fixed-rate quantizer is expected to eliminate some of the complexity of using variable-length scalar quantizers. When transmission of images over noisy channels is considered, our coding scheme does not suffer from error propagation that is typical of coding schemes using variable-length codes, For a set of magnetic resonance (MR) images, coding results obtained from SVQ and ECSQ at low bit rates are indistinguishable. Furthermore, our encoded images are perceptually indistinguishable from the original when displayed on a monitor. This makes our SVQ-based coder an attractive compression scheme for picture archiving and communication systems (PACS). PACS are currently under study for use in an all-digital radiology environment in hospitals, where reliable transmission, storage, and high fidelity reconstruction of images are desired.

关键词： Quantization Biomedical imaging image coding Picture archiving and communication systems image storage Rate-distortion Magnetic noise Magnetic resonance Bit rate Monitoring

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