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Learned image Compression with Quantization Error Compensator

Learned Image Compression with Quantization Error Compensato...

引用

IEEE visual communications and image processing (VCIP)

作者： Nianxiang Fu Zhenzhong Chen Huairui Wang Shan Liu Hubei Luojia Laboratory Wuhan University Wuhan China School of Remote Sensing and Information Engineering Wuhan University Wuhan China Tencent Shenzhen China Tencent America Palo Alto CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recent advancements in learned image compression methods have demonstrated superior rate-distortion performance and remarkable potential compared to traditional compression techniques. However, the core operation of quantization, inherent to lossy image compression, introduces errors that can degrade the quality of the reconstructed image. To address this challenge, we propose a novel Quantization Error Compensator (QEC), which leverages spatial context within latent representations and hyperprior information to effectively mitigate the impact of quantization error. Moreover, we propose a tailored quantization error optimization training strategy to further improve rate-distortion performance. Notably, QEC serves as a lightweight, plug-and-play module, offering high flexibility and seamless integration into various learned image compression methods. Extensive experimental results consistently demonstrate significant coding efficiency improvements achievable by incorporating the proposed QEC into state-of-the-art methods, with a slight increase in runtime.

关键词： Training image coding Quantization (signal) Runtime Codecs visual communication Rate-distortion Distortion Optimization image reconstruction

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Differential image-based Fast and Compatible Convolutional Layers for Multi-core Processors 5

Differential Image-based Fast and Compatible Convolutional L...

引用

5th International conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Hong, Sunghoon Park, Daejin Kyungpook Natl Univ Sch Elect & Elect Engn Daegu South Korea

ISBN: (纸本)9781665456456

Convolutional neural networks with powerful visual image analysis for artificial intelligence are gaining popularity in many research fields, leading to the development of various high-performance algorithms for convolution operators present in these networks. One of these approaches is implemented with general matrix multiplication (GEMM) using the well-known im2col transform for fast convolution operations. In this paper, we propose a multi-core processor-based convolution technique for high-speed convolutional neural networks (CNNs) using differential images. The proposed method improves the convolutional layer's response speed by reducing the computational complexity and using multi-thread technology. In addition, the proposed algorithm has the advantage of being compatible with all types of CNNs. We use the darknet network to evaluate the convolutional layer's performance and show the best performance of the proposed algorithm when using 4-thread parallel processing.

关键词： Convolutional neural networks Convolution techniques Deep learning Fast convolution Multicore processors

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STVP: A Spatiotemporal visual Perception Method for User-generated Content Video Quality Assessment

STVP: A Spatiotemporal Visual Perception Method for User-gen...

引用

IEEE visual communications and image processing (VCIP)

作者： Da Ai Mingyue Lu Jiahao Wang Ying Liu Center for Image and Information Processing Xi’an University of Posts and Telecommunications Xi’an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

With the popularity and development of short video applications, the behavior of using mobile devices to shoot and share user-generated content (UGC) videos has become increasingly common. Video quality assessment (VQA) is critical in guaranteeing end-user viewing experiences. UGC-VQA is a challenging problem due to the complexity and variety of distortion types of UGC videos and the absence of reference videos. To improve the consistency of UGC-VQA results and human subjective ratings, in this paper, we propose a UGC-VQA method based on spatiotemporal visual perception (STVP). Firstly, a hierarchical feature fusion module was added to the feature extraction network to realize the fusion of low-level visual features and high-level semantic features, and obtain the quality perception features with rich visual information. Then, we use the self-attention to weight different frames to distinguish their importance. The long short-term memory (LSTM) network and the time pool are used to model long-term dependencies and temporal memory effects. Experimental results on UGC-VQA datasets show that the proposed method achieves a performance improvement of nearly 2%, and its evaluation results are more consistent with human visual perception.

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Fast Machine Learning Aided Intra Mode Decision for Real-Time VVC Intra Coding

Fast Machine Learning Aided Intra Mode Decision for Real-Tim...

引用

IEEE visual communications and image processing (VCIP)

作者： Joose Sainio Baran Ataman Alban Marie Alexandre Mercat Jarno Vanne Ultra Video Group Tampere University Finland

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Reducing the huge computational complexity of intra mode decision is the key to real-time Video Coding (VVC). This paper proposes a fast intra mode decision scheme that takes advantage of lightweight machine learning (ML) models to classify intra modes into fifteen clusters. The cluster is further refined using one of the three proposed strategies to select the most optimal mode. Our experimental results with the fastest configuration of the practical uvg266 encoder show that the proposed methods yield a competitive rate-distortion-complexity trade-off over a conventional rough mode decision (RMD). To the best of our knowledge, this is the first work to successfully reduce the complexity of RMD in a practical VVC encoder with the use of ML techniques.

关键词： Video coding Logistic regression image coding visual communication Computational modeling Machine learning Predictive models Real-time systems Proposals Computational complexity

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JADD-GAN: A Joint Attention Generative Adversarial Data Fusion Network for Object Detection and Tracking 24

JADD-GAN: A Joint Attention Generative Adversarial Data Fusi...

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24th IEEE International conference on High Performance Computing and communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xu, Guoxia Wang, Hao Zhao, Meng Zhu, Hu Norwegian University of Science and Technology Department of Computer Science Gjovik Norway School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9798350319934

image fusion is the fusion of images captured by different sensors to generate a single image with enhanced information, and fusion technology, as one of the important branches in the field of information fusion, mainly realizes the processing of multi-source image information. However, many commonly used fusion methods usually ignore the visual naturalness and information fidelity of the fused images and lack emphasis on the salient information, which makes the fused images unsuitable for human visual perception. To address these shortcomings of existing methods, in this paper, we propose the Joint Attention and Dual Discriminator Generative Adversarial Data Fusion Network JADD-GAN. In the generator module, to increase the extraction of multi-level information by the network, we firstly adopt a dual encoder structure and give information fusion in the decoder part. Secondly, different discriminators are used for infrared and visible images in order to highlight the thermal radiation information and key textures. The effectiveness of the method is verified by experiments on four datasets, and the results show that the method can effectively highlight the thermal radiation information and key texture details of the fused images, fully demonstrating its great potential and performance in solving the infrared and visible images fusion (IVF) problem. © 2022 IEEE.

关键词： Generative adversarial networks

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Early Determination for Intra Block Copy Prediction with Refined Screen Content Detection

Early Determination for Intra Block Copy Prediction with Ref...

引用

IEEE visual communications and image processing (VCIP)

作者： Zhenhao Sun Meng Wang Yingwen Zhang Shiqi Wang Sam Kwong Department of Computer Science City University of Hong Kong China School of Data Science Lingnan University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The intra block copy (IBC) mode introduced can significantly improve the coding efficiency for screen content. However, with the mixed content, the coding performance improvement of IBC is limited and while also introducing the increases of the coding complexity. This paper proposes an early determination scheme for IBC prediction to reduce the encoding complexity over mixed content sequences containing captured content regions. Our method is based on a refined CTU-level screen content detection method, which can better distinguish screen content in mixed scenes. Experimental results show that the proposed method reduces encoding complexity with moderate coding performance loss on mixed content sequences.

关键词： Video coding image coding visual communication Semantics Text detection Complexity theory

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Real-Time Video-based Point Cloud Compression

Real-Time Video-based Point Cloud Compression

引用

IEEE visual communications and image processing (VCIP)

作者： Louis Fréneau Guillaume Gautier Heikki Tampio Alexandre Mercat Jarno Vanne Ultra Video Group Tampere University Tampere Finland

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

This paper presents a demonstration setup for our open-source intra encoder called uvgVPCCenc, which is optimized for real-time Video-based Point Cloud Compression (V-PCC). uvgVPCCenc achieves an average encoding speed of 26 frames per second (fps) on an Intel i7-12700 CPU when encoding volumetric video sequences with up to 185 000 points per frame. It is shown to be 700 times as fast as TMC2 reference implementation for V-PCC. Our work is the first to demonstrate real-time intra V-PCC encoding on a consumer-grade desktop computer. It indicates that even the immense computational complexity of intra V-PCC encoding can be tackled for practical applications with effective design and optimization techniques.

关键词： Point cloud compression image coding visual communication Video sequences Streaming media Real-time systems Computational complexity Optimization

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An intricate spectrogram processing in speech enhancement using machine learning with cloud computing 7

An intricate spectrogram processing in speech enhancement us...

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7th International conference on image Information processing, ICIIP 2023

作者： Reddy, Yenna Geetha Mahendran, G. Al-Khaykan, Ameer Alkawak, Omar A. Kalaiselvan, S.A. Priya, A. Department of Computer Science And Engineering Kompally Telangana Secunderabad India R.M.K Engineering College Department of Mechancal Engineering Chennai Tamil Nadu Kavaraipettai India University of Kufa Department of Electronic And Communications Engineering Najaf Iraq Al-Mustaqbal University College Department of Air Conditioning And Refrigeration Technical Engineering Babil Hilla Iraq Department of Computer Science And Engineering Tamil Nadu Chennai India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India

ISBN: (纸本)9798350371406

The spectrogram is defined as the visual representation of the signal strength with a particular interval of time regarding frequencies in the appropriate waveform. It is demonstrated using the Fourier transform. It is illustrated in a three-dimensional plot of signal composed with amplitude versus frequency and time. They are classified as long signal sections and short signal sections with filters. It demonstrates the consecutive spectacle of the sound signal at a particular interval of time. The time-frequency representation of audio signals is obtained through spectrograms. This helps in the understanding of the speech data. Machine learning plays an important role in the identification of the relevant features in the spectrogram. The intricate patterns in the time and frequency domains are represented through CNN and RNN. These models are trained and tested through a large dataset which includes both clean and noisy speech samples. This helps to learn complex interrelationships and patterns associated with varied environmental conditions and interference systems. The computational demands of these systems are handled through the cloud computing techniques. They provide a platform for effective solutions for performing real-time interference. These cloud services provide various collaborations to numerous models to achieve desired outcomes. The proposed system provides higher adaptability in varied environmental and noisy profiles. Thus the integration of cloud computing with machine learning provides real-time processing with a natural seamless user experience. © 2023 IEEE.

关键词： Cloud computing

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A High-Efficiency and Low-Complexity SKIP Type for Base Mesh Coding in V-DMC

A High-Efficiency and Low-Complexity SKIP Type for Base Mesh...

引用

IEEE visual communications and image processing (VCIP)

作者： Jianfeng Xu Haruhisa Kato Kei Kawamura Advanced Visual Communication Laboratory KDDI Research Inc. Fujimino-shi Saitama Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Video-based Dynamic Mesh Coding (V-DMC) is an emerging standard for dynamic mesh compression, where the original meshes are decimated into simplified meshes called base meshes. This paper introduces a novel SKIP type for base mesh coding in V-DMC, complementing the existing INTRA and INTER types. When the SKIP type is used in base mesh coding, it directly copies the reconstructed base mesh from the reference frame, eliminating the need for additional data coding. Thus, the reconstructed base mesh in the current frame is identical to that in the reference frame. This significantly reduces the bit rate and decoding time for base meshes. Additionally, this paper employs a Lagrangian cost function using a linear model for bit estimation of INTRA type and L1 norms for distortion approximation of SKIP type to enable the encoder to select the best type for base meshes. Experimental results demonstrate superior BD-rate performance and significantly reduced decoding time for base meshes using the SKIP type, particularly in sequences with minimal object movements.

关键词： image coding visual communication Bit rate Estimation Cost function Distortion Decoding image reconstruction Standards

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Binocular Rivalry and Fusion Mechanisms Based No-Reference Stereoscopic image Quality Assessment Considering Feedback Guidance

Binocular Rivalry and Fusion Mechanisms Based No-Reference S...

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IEEE visual communications and image processing (VCIP)

作者： Haoxiang Chang Sumei Li Dongyue He Chen Zhang Tianjin University Tianjin China

With the growing popularity of 3D content and virtual reality applications, effective no-reference stereoscopic image quality assessment (NR-SIQA) methods have become increasingly important. In this paper, we propose a convolutional neural network (CNN) SIQA model based on binocular rivalry and fusion mechanisms of the human visual system (HVS). In order to get a better representation of binocular information, we propose Two-Stage Enhanced Fusion Module (TSEFM) that consists of two stages for monocular features enhancement and binocular features fusion, respectively. Given the dynamical characteristics of binocular rivalry phenomenon, the proposed Content-Aware Binocular Rivalry Fusion Module (CABRFM) dynamically and adaptively adjusts its output based on the input content. Additionally, considering that feedback mechanism of HVS is indispensable and significant, we introduce feedback connections during feature aggregation to realize the guidance of high-level features to low-level features. Extensive experimental results demonstrate the superiority of our method over state-of-the-art metrics, showcasing its excellent performance.

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