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A Diverse Environment Coal Gangue image Segmentation Model Combining Improved U-Net and Semi-supervised Automatic Annotation 8th

A Diverse Environment Coal Gangue Image Segmentation Model C...

引用

8th International conference on Cognitive Systems and Information processing, ICCSIP 2023

作者： Liu, Xiuhua Zhu, Wenbo Zhu, Zhengjun Luo, Lufeng Zhang, Yunzhi Lu, Qinghua School of Mechatronic Engineering and Automation Foshan University Foshan528000 China China Coal Technology Engineering Group Tangshan Research Institute Tangshan063000 China

ISBN: (纸本)9789819980208

The problem of uneven illumination in the coal gangue image and the existence of fine coal gangue reduces the accuracy of the coal gangue location, which also makes it difficult to the annotation of the coal gangue images. In this paper, we propose an improved U-Net gangue segmentation algorithm to achieve gangue sorting and automatic image annotation under diversified environments. Specifically, to solve the problem that some fine coal and gangue fragments are difficult to be recognized, the InceptionV1 module is introduced instead of some of these convolutional blocks. Secondly, to solve the visual blur caused by light changes in industrial sites, the CPAM attention module is added to the fusion part of U-Net. Finally, to alleviate the difficulty of coal gangue image annotation, a semi-supervised human-in-the-loop automatic annotation framework is constructed, and a statistical method based on classification probability is proposed to automatically screen the questionable labels. The experimental results show that the proposed improved segmentation model exhibits good performance in the coal gangue sorting task with MIoU values of 80.23% and MPA values of 86.16%, which have higher accuracy compared to the original model. In addition, the statistical method proposed in this study can effectively screen out the incorrect and correct labeled data, thus enabling automatic annotation and screening. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image annotation

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Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization

Bit Distribution Study and Implementation of Spatial Quality...

引用

IEEE visual communications and image processing (VCIP)

作者： Panqi Jia Jue Mao Esin Koyuncu A. Burakhan Koyuncu Timofey Solovyev Alexander Karabutov Yin Zhao Elena Alshina André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nürnberg Germany Huawei Technologies Chair of Media Technology Technical University of Munich Germany

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Currently, there is a high demand for neural network-based image compression codecs. These codecs employ non-linear transforms to create compact bit representations and facilitate faster coding speeds on devices compared to the handcrafted transforms used in classical frameworks. The scientific and industrial communities are highly interested in these properties, leading to the standardization effort of JPEG-AI. The JPEG-AI verification model has been released and is currently under development for standardization. Utilizing neural networks, it can outperform the classic codec VVC intra by over 10% BD-rate operating at base operation point. Researchers attribute this success to the flexible bit distribution in the spatial domain, in contrast to VVC intra’s anchor that is generated with a constant quality point. However, our study reveals that VVC intra displays a more adaptable bit distribution structure through the implementation of various block sizes. As a result of our observations, we have proposed a spatial bit allocation method to optimize the JPEG-AI verification model’s bit distribution and enhance the visual quality. Furthermore, by applying the VVC bit distribution strategy, the objective performance of JPEG-AI verification mode can be further improved, resulting in a maximum gain of 0.45 dB in PSNR-Y.

关键词： visualization Codecs image coding Distribution strategy visual communication Shape Bit rate Standardization Transforms Optimization

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Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation

Cross-resolution Face Recognition via Identity-Preserving Ne...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuhang Lu Touradj Ebrahimi Multimedia Signal Processing Group (MMSPG) École Polytechnique Fédérale de Lausanne (EPFL)

Cross-resolution face recognition has become a challenging problem for modern deep face recognition systems. It aims at matching a low-resolution probe image with high-resolution gallery images registered in a database. Existing methods mainly leverage prior information from high-resolution images by either reconstructing facial details with super-resolution techniques or learning a unified feature space. To address this challenge, this paper proposes a new approach that enforces the network to focus on the discriminative information stored in the low-frequency components of a low-resolution image. A cross-resolution knowledge distillation paradigm is first employed as the learning framework. Then, an identity-preserving network, WaveResNet, and a wavelet similarity loss are designed to capture low-frequency details and boost performance. Finally, an image degradation model is conceived to simulate more realistic low-resolution training data. Consequently, extensive experimental results show that the proposed method consistently outperforms the baseline model and other state-of-the-art methods across a variety of image resolutions.

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ALICE: Adapt your Learnable image Compression modEl for variable bitrates

ALICE: Adapt your Learnable Image Compression modEl for vari...

引用

IEEE visual communications and image processing (VCIP)

作者： Gabriele Spadaro Muhammad Salman Ali Alberto Presta Giommaria Pilo Sung-Ho Bae Jhony H. Giraldo Attilio Fiandrotti Marco Grangetto Enzo Tartaglione LTCI Télécom Paris Institut Polytechnique de Paris France University of Turin Italy Kyung Hee University Republic of Korea

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

When training a Learned image Compression model, the loss function is minimized such that the encoder and the decoder attain a target Rate-Distorsion trade-off. Therefore, a distinct model shall be trained and stored at the transmitter and receiver for each target rate, fostering the quest for efficient variable bitrate compression schemes. This paper proposes plugging Low-Rank Adapters into a transformer-based pre-trained LIC model and training them to meet different target rates. With our method, encoding an image at a variable rate is as simple as training the corresponding adapters and plugging them into the frozen pre-trained model. Our experiments show performance comparable with state-of-the-art fixed-rate LIC models at a fraction of the training and deployment cost. We publicly released the code at https://***/EIDOSLAB/ALICE.

关键词： Training Adaptation models image coding Costs visual communication Transmitters Bit rate Receivers Transformers Decoding

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Variable-rate Learned image Compression with Adaptive Quantization Step Size

Variable-rate Learned Image Compression with Adaptive Quanti...

引用

IEEE visual communications and image processing (VCIP)

作者： Feihong Mei Li Li Dong Liu Deep Space Exploration Laboratory USTC Hefei China

Nowadays, learned image compression has outperformed traditional coding methods like VVC. However, learned image compression methods are often optimized for specific rates and lack support for variable rate compression. Though some efforts have been made to address variable rate compression, striking the right balance between performance and complexity remains a challenge. To address this, we propose the Adaptive Quantize Step Size (AQS) technique, allowing continuous variable rate within a single model. Unlike methods that learn to scale the latent representation, AQS enables learning of different quantization step sizes for different rates, preserving the latent representation’s distribution while only affecting the quantization step size, which is beneficial for the entropy model. As a result, our method achieves superior performance compared to existing methods while maintaining similar complexity. It delivers comparable results across a wide range of rates using a single model with minimal additional parameters.

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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...

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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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