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Overview of Variable Rate Coding in JPEG AI

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Jia, Panqi Brand, Fabian Yu, Dequan Karabutov, Alexander Alshina, Elena Kaup, Andre Friedrich-Alexander University Erlangen-Nürnberg Department of Electrical-Electronic-Communication Engineering Chair of Multimedia Communications and Signal Processing Erlangen91058 Germany Huawei Munich Research Center Munich80992 Germany

Empirical evidence has demonstrated that learning-based image compression can outperform classical compression frameworks. This has led to the ongoing standardization of learned-based image codecs, namely Joint Photographic Experts Group (JPEG) AI. The objective of JPEG AI is to enhance compression efficiency and provide a software and hardware-friendly solution. Based on our research, JPEG AI represents the first standardization that can facilitate the implementation of a learned image codec on a mobile device. This article presents an overview of the variable rate coding functionality in JPEG AI, which includes three variable rate adaptations: a three-dimensional quality map, a fast bit rate matching algorithm, and a training strategy. The variable rate adaptations offer a continuous rate function up to 2.0 bpp, exhibiting a high level of performance, a flexible bit allocation between different color components, and a region of interest function for the specified use case. The evaluation of performance encompasses both objective and subjective results. With regard to the objective bit rate matching, the main profile with low complexity yielded a 13.1% BD-rate gain over VVC intra, while the high profile with high complexity achieved a 19.2% BD-rate gain over VVC intra. The BD-rate result is calculated as the mean of the seven perceptual metrics defined in the JPEG AI common test conditions. With respect to subjective results, the example of improving the quality of the region of interest is illustrated. © 1991-2012 IEEE.

关键词： Image compression

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Rational polynomial windows with high attenuation of sidelobes

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Archives of Electrical engineering 2012年第1期61卷 7-14页

作者： Okarma, Krzysztof Department of Signal Processing and Multimedia Engineering Faculty of Electrical Engineering West Pomeranian University of Technology Szczecin Poland

In the paper the idea of rational polynomial windows optimised towards low level of Fourier spectrum's sidelobes is presented. A relevant advantage of the polynomial windows family and their modifications is their ability to easily change their properties changing only the values of the polynomial coefficients. The obtained frequency characteristics demonstrate better properties of proposed rational windows than their standard polynomial equivalents requiring only the additional division operation. Such approach does not increase the computational complexity in significant way and the great advantage of polynomial windows which is their low computational complexity is preserved.

关键词： Spectrum analysis

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Optimal thresholds for multiwavelet shrinkage

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ELECTRONICS LETTERS 2003年第5期39卷 473-474页

作者： Hsung, TC Lun, DPK Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

Optimal thresholds selection for multiwavelet denoising using multivariate shrinkage is considered. Unlike other risk estimators which are derived for a single threshold, a new risk estimator is proposed for each deco... 详细信息

关键词： Wavelets (Mathematics) signal processing

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Microphone arrays application in three-dimensional sound source localisation

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International Journal of Intelligent Information and Database Systems 2012年第5期6卷 436-450页

作者： Kornatowski, Eugeniusz Department of Signal Processing and Multimedia Engineering Faculty of Electrical Engineering West Pomeranian University of Technology 70-310 Szczecin Poland

This article describes the problems of use of spatial microphone arrays in sound source location. Questions concerning modelling of planar (2-D) and spatial (3-D) directional characteristics of microphone arrays are discussed. Typical algorithms used in SSL system are also described. The paper describes options to use a single point surround microphone to determine a direction of the sound source localisation. A 'soundfield' microphone with four transducers (capsules) characterised by cardioid directivity pattern. A unique mechanical design of the transducer results in its omnidirectional directivity pattern. The microphone enables 3-D sound acquisition in so called A-format. Upon further processing of signal it is possible to determine, e.g., a direction of a sound source within space. The conducted experiments prove that a simple calculation algorithm is in particular feasible for a real time operation, and application of the soundfield microphone significantly simplifies mechanical design of the SSL system. Copyright © 2012 Inderscience Enterprises Ltd.

关键词： Microphone array

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Air-gap data transmission using screen brightness modulation

Air-gap data transmission using screen brightness modulation

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2018 International Interdisciplinary PhD Workshop, IIPhDW 2018

作者： Bak, Dawid Mazurek, Przemyslaw West Pomeranian University of Technology Szczecin Department of Signal Processing and Multimedia Engineering Poland

ISBN: (纸本)9781538661437

Air-gap data is important for the security of computer systems. The injection of the computer virus is limited but possible, however data communication channel is necessary for the transmission of stolen data. This paper considers BFSK digital modulation applied to brightness changes of screen for unidirectional transmission of valuable data. Experimental validation and limitations of the proposed technique are provided. © 2018 IEEE.

关键词： Demodulation

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Information technology applications in biomedical functional imaging

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IEEE Transactions on Information Technology in Biomedicine 1999年第3期3卷 221-230页

作者： Feng, Dagan Biomedical and Multimedia Information Technology Group Department of Computer Science University of Sydney NSW 2006 Australia Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong

In parallel with rapid advances in computer technology, biomedical functional imaging is having an ever-increasing impact on healthcare. Functional imaging allows us to see dynamic processes quantitatively in the living human body. However, as we need to deal with four-dimensional time-varying images, space requirements and computational complexity are extremely high. This makes information management, processing, and communication difficult. Using the minimum amount of data to represent the required information, developing fast algorithms to process the data, organizing the data in such a way as to facilitate information management, and extracting the maximum amount of useful information from the recorded data have become important research tasks in biomedical information technology. For the last ten years, the Biomedical and multimedia Information Technology (BMIT) Group and, recently, the Center for multimedia signal processing have conducted systematic studies on these topics. Some of the results relating to functional imaging data acquisition, compression, storage, management, processing, modeling, and simulation are briefly reported in this paper.

关键词： Medical imaging

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MCFTNet: Multimodal Cross-layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Fast multiscale directional filter bank with applications to texture retrieval

Fast multiscale directional filter bank with applications to...

引用

4th International Conference on Information Technology and Applications, ICITA 2007

作者： Cheng, K.O. Law, N.F. Siu, W.C. Centre for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University

ISBN: (纸本)0980326702

The contourlet transform has emerged recently as an efficient directional multiresolution image decomposition. In order to enhance its distinguishing power for texture images, the high frequency band is split by half and results in the multiscale directional filter bank (MDFB). However, this raises the computational complexity considerably because an extra set of scale and directional decomposition is required to perform on the full image size. In this paper, we develop a new framework for the MDFB, in which directional decomposition is performed prior to the scale decomposition in the first two scales. Experiments show that 30.6%-33.2% saving in computational time can be achieved. Meanwhile, it has the same flexibility in frequency partitioning and similar texture retrieval performance as the original algorithm of the MDFB.

关键词： Filter banks

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Spectral approaches for DNA sequence classification

Spectral approaches for DNA sequence classification

引用

4th International Conference on Information Technology and Applications, ICITA 2007

作者： Cheng, K.O. Law, N.F. Siu, W.C. Centre for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University

ISBN: (纸本)0980326702

Z-curve features are one of the popular features used in DNA sequence classification. Here, we studied the Z-curve features from a signal processing point of view. In particular, the Z-curve features are re-interpreted through a spectral formulation. Our analysis showed that there are significant differences in the spectral interpretation between the Z-curve formulation and the FFT (Fast Fourier Transform) approach. From the spectral formulation of the Z-curve approach, we obtained three modified sequences that characterize different biological properties which are useful for coding region prediction. Spectral analysis on the modified sequences showed a much more prominent three-periodicity property in coding regions than using the FFT approach. Our experiments indicated that for long sequences, prominent peaks at 2II/3 are observed at coding regions. For short sequences, peaks can still be observed at coding regions. We also obtained good classification performance using the spectral features derived from the three modified sequences.

关键词： DNA sequences

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A semi-parametric hybrid neural model for nonlinear blind signal separation.

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International journal of neural systems 2000年第2期10卷 79-93页

作者： Peng, H. Chi, Z. Siu, W. Center for Multimedia Signal Processing Department of Electronic &amp Information Engineering The Hong Kong Polytechnic University Kowloon Hong Kong

Nonlinear blind signal separation is an important but rather difficult problem. Any general nonlinear independent component analysis algorithm for such a problem should specify which solution it tries to find. Several recent neural networks for separating the post nonlinear blind mixtures are limited to the diagonal nonlinearity, where there is no cross-channel nonlinearity. In this paper, a new semi-parametric hybrid neural network is proposed to separate the post nonlinearly mixed blind signals where cross-channel disturbance is included. This hybrid network consists of two cascading modules, which are a neural nonlinear module for approximating the post nonlinearity and a linear module for separating the predicted linear blind mixtures. The nonlinear module is a semi-parametric expansion made up of two sub-networks, one of which is a linear model and the other of which is a three-layer perceptron. These two sub-networks together produce a "weak" nonlinear operator and can approach relatively strong nonlinearity by tuning parameters. A batch learning algorithm based on the entropy maximization and the gradient descent method is deduced. This model is successfully applied to a blind signal separation problem with two sources. Our simulation results indicate that this hybrid model can effectively approach the cross-channel post nonlinearity and achieve a good visual quality as well as a high signal-to-noise ratio in some cases.

关键词：算法图像处理计算机辅助/方法模型神经学神经网络(计算机) 非线性动力学信号传导/生理学

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