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Low-complexity Transform Network Architecture for JPEG AI image Codec

Low-complexity Transform Network Architecture for JPEG AI Im...

引用

IEEE visual communications and image processing (VCIP)

作者： Xiang Pan Ding Ding Liqiang Wang Xiaozhong Xu Shan Liu Tencent Media Lab Shenzhen China Tencent Media Lab Palo Alto CA USA

Learning-based image coding schemes, exemplified by JPEG AI, have shown potential by greatly exceeding the conventional image compression standards in rate-distortion (RD) performance. However, their widespread applications are hindered by high decoding complexity, particularly from the upsampling and attention modules. Existing works sought to reduce this complexity, but their solutions are not fully effective, leaving considerable complexity unaddressed. In this paper, we present a simplified transform network architecture that employs an optimized attention module, a streamlined upsampling module, and a pared-down activation function to tackle this issue. Simulation results show that the simplified decoder sees its complexity (measured by kMACs/pixel) reduced by 80% (from 833 to 172), while the gain over Versatile Video Coding (VVC) increases slightly (from 27.3% to 27.5%). Partial methods in this paper have been integrated into the JPEG AI Verification Model (VM) software.

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No-Reference Point Cloud Quality Assessment with Adaptive Keyframe Selection

No-Reference Point Cloud Quality Assessment with Adaptive Ke...

引用

IEEE visual communications and image processing (VCIP)

作者： Haomiao Wang Xiaochuan Wang Xianpeng Yuan Xianming Chen Haisheng Li School of Computer and Artificial Intelligence Beijing Technology and Business University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In recent years, projection-based no-reference point cloud quality metrics have shown remarkable performance on public datasets. These metrics involve projecting the point cloud onto a 2D space from multiple viewpoints and applying traditional image quality metrics to the resulting projected images. However, these existing methods suffer from a measurable projection strategy, which would induce information loss or redundancy leading to training bias and reduced generalizability. To overcome these limitations, we propose AKSNet, a novel no-reference point cloud quality metric that utilizes an adaptive keyframe selection strategy. In particular, we treat the process of point cloud projection as video sequences captured from successive viewpoints and design a keyframe selection module to pick the most representative frames. Based on the selected frames, we can subsequently predict the quality score of the point cloud. Experimental results demonstrate that our method achieves competitive performance in comparison with state-of-the-art methods. While the proposed adaptive keyframe selecting strategy can effectively promote the generalization performance.

关键词： Point cloud compression Training image quality visual communication image processing Video sequences Redundancy Particle measurements Loss measurement Quality assessment

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Frequency-Aware Hierarchical image Compression for Humans and Machines

Frequency-Aware Hierarchical Image Compression for Humans an...

引用

IEEE visual communications and image processing (VCIP)

作者： Yue Luo Zixiang Zhang Jinhao Kuang Li Yu Huazhong University of Science and Technology Wuhan China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

To achieve efficient compression for both human vision and machine perception, scalable coding methods have been proposed in recent years. However, existing methods do not fully eliminate the redundancy between features corresponding to different tasks, resulting in suboptimal coding performance. In this paper, we propose a frequency-aware hierarchical image compression framework designed for humans and machines. Specifically, we investigate task relationships from a frequency perspective, utilizing only HF information for machine vision tasks and leveraging both HF and LF features for image reconstruction. Besides, the residual block embedded octave convolution module is designed to enhance the information interaction between HF features and LF features. Additionally, a dual-frequency channel-wise entropy model is applied to reasonably exploit the correlation between different tasks, thereby improving multi-task performance. The experiments show that the proposed method offers -69.3%∼-75.3% coding gains on machine vision tasks compared to the relevant benchmarks, and -19.1% gains over state-of-the-art scalable image codec in terms of image reconstruction quality.

关键词： image coding Correlation Codecs Convolution visual communication Machine vision Redundancy Multitasking Entropy image reconstruction

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Towards Efficient Learned image Coding for Machines via Saliency-Driven Rate Allocation

Towards Efficient Learned Image Coding for Machines via Sali...

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IEEE visual communications and image processing (VCIP)

作者： Zixiang Zhang Li Yu Qiang Zhang Junjun Mei Tao Guan Huazhong University of Science and Technology Wuhan China ZTE Corporation Nanjing China

Recently, efficient image coding for machines method is widely required under machine vision task-oriented coding scenarios. To achieve higher task accuracy under a certain bitrate constraint, existing solutions take advantage of cutting-edge learned image compression methods and cascade the compression and specific task model straightly to carry out the joint optimization, which lacks adaptability and flexibility to handling machine vision saliency and non-saliency regions, thus obtaining inferior performance. In this paper, we propose a learned image compression model with a saliency-driven rate allocation strategy for machine vision. Specifically, an auxiliary network is introduced to provide reference latent first which contains full image reconstruction required information. And then, a machine vision saliency detector is applied to generate the adjustment factor to achieve rate allocation in the latent domain. In addition, a spatial dynamic convolution module is designed to facilitate the compressor adaptively processing the different spatial areas. Finally, the multi-distortion constraint is utilized to achieve an end-to-end optimization. Experimental results show that the proposed method achieves -77.48% coding gains over VVC using Mask R-CNN as the back-end task network on the Cityscapes dataset. Moreover, the reconstructed image maintains considerable visual fidelity, making it possible both for human and machine consumption.

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Performance Evaluation of Feature Detectors and Descriptors with Close-Range Solar Panel images

Performance Evaluation of Feature Detectors and Descriptors ...

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IEEE visual communications and image processing (VCIP)

作者： Eman Ansar Sara Sewil Fathimath Zuha Maksood Eduardo Feo-Flushing School of Computer Science Carnegie Mellon University Carnegie Mellon University in Qatar

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recent advancements in photovoltaic (PV) systems for power generation necessitate continuous inspection, fault detection, and maintenance to maximize energy production. While prior research has primarily focused on image processing and algorithms using long-range shots of PV systems, this study evaluates existing feature detectors and descriptors for processing close-range thermal images of solar panels. Utilizing a dataset captured with the FLIR Duo Pro R camera, which includes variations such as camera angle rotation, motion blur, and different times of day, we assessed the performance of various feature detectors (SIFT, FAST, AKAZE, BRISK, ORB) and descriptors (SIFT, FREAK, ORB, SURF, BRISK) with FAST as a keypoint detector. Additionally, the study examines the impact of different color maps (JET, RAINBOW, HOT, COOL, BONE, GRAY) on these metrics. Our results indicate that AKAZE is the most effective detector, while SURF is the most robust descriptor, particularly when used with multichromatic, high-contrast color maps like JET and RAINBOW. These findings provide a basis for improving various applications, such as image stitching and object detection, and have potential future work in PV fault detection.

关键词： Performance evaluation image color analysis visual communication Fault detection Detectors Production Object detection Feature extraction Cameras Solar panels

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A Secure Network Coding based image communications in Two-Hop Wireless Relay Networks

A Secure Network Coding based Image Communications in Two-Ho...

引用

IEEE visual communications and image processing (VCIP)

作者： Quoc-Tuan Vien Tuan T. Nguyen Faculty of Science and Technology Middlesex University UK School of Computing and Mathematical Sciences University of Greenwich UK

This paper investigates the image communications in two-hop wireless relay networks (TH-WRNs) where a source node sends images to a destination node with the assistance of a relay node via two hops, i.e. source-to-relay and relay-to-destination links. Due to the broadcast nature of wireless media, there exists an eavesdropper who tries to overhear and recover the images. Aiming to enhance the security and also to save transmission bandwidth of the image communications, we propose a secure relaying transmission (SRT) protocol by exploiting both random linear network coding (RLNC) and image super-resolution (ISR) techniques. In the proposed protocol, the original high-resolution (HR) images are downscaled at the source node and the RLNC is employed at both the source and relay nodes to conceal the original images from the eavesdropper. The RLNC decoding and ISR are adopted at the destination node to decode and recover the HR images, while the eavesdropper cannot decode the images due to the unawareness of the coefficient matrices and the reference images in the RLNC. It is shown that the proposed SRT protocol achieves a significantly higher performance at the destination than at the eavesdropper. Furthermore, with high-quality relaying hops, the SRT protocol outperforms the secure direct transmission (SDT) protocol with only a direct link between the source and the destination nodes. Finally, simulation results are provided to verify the findings.

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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...

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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...

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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...

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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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