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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Shaokang Xiang, Guoqing Xu, Jinchang Zhang, Shanghang Xie, Xiaodong School of Computer Science Peking University Beijing China Alibaba Cloud Computing Beijing China

ISBN: (纸本)9798350368741

neural image compression has achieved significant advancements, consistently outperforming traditional codecs in terms of performance. However, research on quality control algorithms for neural image compression is still lacking. In this paper, we propose a framework designed to control the quality of compressed images through a one-pass pre-analysis. First, we construct a foundational relationship between the quantization factor and compression distortion, utilizing variable rate neural image compression as the basis for quality control. Second, we introduce the image Content-compression features-based Distortion Estimation Network (C2DEN) to efficiently fit the sample-adaptive Quantization-Distortion (QD) model. Leveraging the QD model, we convert the target quality into a quantization factor to control the compression model, enabling quality-controllable compression of samples. Experimental results show that the average quality errors on four different datasets are only 0.89%, 1.79%, 1.73%, and 1.61%. Compared with existing control methods, our method reduces the algorithm time complexity by 98.58%, 98.52%, 98.85%, and 98.50% while ensuring accuracy, which further demonstrates the superiority of our method. © 2025 IEEE.

关键词： linear model neural image compression quality control quantization factor

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neural image compression for non-small cell lung cancer subtype classification in H&E stained whole-slide images

Neural image compression for non-small cell lung cancer subt...

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Medical Imaging Conference - Digital Pathology

作者： Aswolinskiy, Witali Tellez, David Raya, Gabriel van der Woude, Lieke Looijen-Salamon, Monika van der Laak, Jeroen Grunberg, Katrien Ciompi, Francesco Radboud Univ Nijmegen Computat Pathol Grp Med Ctr Nijmegen Netherlands

ISBN: (纸本)9781510640368

Classification of non-small-cell lung cancer (NSCLC) into adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) via histopathology is a vital prerequisite to select the appropriate treatment for lung cancer patients. Most machine learning approaches rely on manually annotating large numbers of whole slide images (WSI) for training. However, manually delineating cancer areas or even single cancer cells on hundreds or thousands of slides is tedious, subjective and requires highly trained pathologists. We propose to use neural image compression (NIC), which requires only slide-level labels, to classify NSCLC into LUSC and LUAD. NIC consists of two phases/networks. In the first phase the slides are compressed with a convolutional neural network (CNN) acting as an encoder. In the second phase the compressed slides are classified with a second CNN. We trained our classification model on >2,000 NIC-compressed slides from the TCGA and TCIA databases and evaluated the model performance additionally on several internal and external cohorts. We show that NIC approaches state of the art performance on lung cancer classification, with an average AUC of 0.94 on the TCGA and TCIA testdata, and AUCs between 0.84 and 0.98 on other independent datasets.

关键词： Computational Pathology Lung cancer Deep learning neural image compression

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Toward Robust neural image compression: Adversarial Attack and Model Finetuning

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第12期33卷 7842-7856页

作者： Chen, Tong Ma, Zhan Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Jiangsu Peoples R China

Deep neural network-based image compression has been extensively studied. However, the model robustness which is crucial to practical application is largely overlooked. We propose to examine the robustness of prevailing learned image compression models by injecting negligible adversarial perturbation into the original source image. Severe distortion in decoded reconstruction reveals the general vulnerability in existing methods regardless of their settings (e.g., network architecture, loss function, quality scale). A variety of defense strategies including geometric self-ensemble based pre-processing, and adversarial training, are investigated against the adversarial attack to improve the model's robustness. Later the defense efficiency is further exemplified in real-life image recompression case studies. Overall, our methodology is simple, effective, and generalizable, making it attractive for developing robust learned image compression solutions. All materials are made publicly accessible at https://***/RobustNIC for reproducible research.

关键词： neural image compression model robustness adversarial attack adversarial training

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FREQUENCY DISENTANGLED FEATURES IN neural image compression 30

FREQUENCY DISENTANGLED FEATURES IN NEURAL IMAGE COMPRESSION

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30th IEEE International Conference on image Processing (ICIP)

作者： Zafari, Ali Khoshkhahtinat, Atefeh Mehta, Piyush Saadabadi, Mohammad Saeed Ebrahimi Akyash, Mohammad Nasrabadi, Nasser M. West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9781728198354

The design of a neural image compression network is governed by how well the entropy model matches the true distribution of the latent code. Apart from the model capacity, this ability is indirectly under the effect of how close the relaxed quantization is to the actual hard quantization. Optimizing the parameters of a rate-distortion variational autoencoder (R-D VAE) is ruled by this approximated quantization scheme. In this paper, we propose a feature-level frequency disentanglement to help the relaxed scalar quantization achieve lower bit rates by guiding the high entropy latent features to include most of the low-frequency texture of the image. In addition, to strengthen the de-correlating power of the transformer-based analysis/synthesis transform, an augmented self-attention score calculation based on the Hadamard product is utilized during both encoding and decoding. Channel-wise autoregressive entropy modeling takes advantage of the proposed frequency separation as it inherently directs high-informational low-frequency channels to the first chunks and conditions the future chunks on it. The proposed network not only outperforms hand-engineered codecs, but also neural network-based codecs built on computation-heavy spatially autoregressive entropy models.

关键词： neural image compression frequency disentangled features transformer-based transform coding augmented self-attention

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Extending Unsupervised neural image compression With Supervised Multitask Learning 3

Extending Unsupervised Neural Image Compression With Supervi...

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3rd Conference on Medical Imaging with Deep Learning (MIDL)

作者： Tellez, David Hoppener, Diederik Verhoef, Cornelis Grunhagen, Dirk Nierop, Pieter Drozdzal, Michal van der Laak, Jeroen Ciompi, Francesco Radboud Univ Nijmegen Dept Pathol Med Ctr Nijmegen Netherlands Erasmus MC Inst Canc Dept Surg Oncol & Gastrointestinal Surg Nijmegen Netherlands Facebook AI Res Cambridge MA USA

We focus on the problem of training convolutional neural networks on gigapixel histopathology images to predict image-level targets. For this purpose, we extend neural image compression (NIC), an image compression framework that reduces the dimensionality of these images using an encoder network trained unsupervisedly. We propose to train this encoder using supervised multitask learning (MTL) instead. We applied the proposed MTL NIC to two histopathology datasets and three tasks. First, we obtained state-of-the-art results in the Tumor Proliferation Assessment Challenge of 2016 (TUPAC16). Second, we successfully classified histopathological growth patterns in images with colorectal liver metastasis (CLM). Third, we predicted patient risk of death by learning directly from overall survival in the same CLM data. Our experimental results suggest that the representations learned by the MTL objective are: (1) highly specific, due to the supervised training signal, and (2) transferable, since the same features perform well across different tasks. Additionally, we trained multiple encoders with different training objectives, e.g. unsupervised and variants of MTL, and observed a positive correlation between the number of tasks in MTL and the system performance on the TUPAC16 dataset.

关键词： neural image compression supervised multitask learning histopathology

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EXPLORING STRUCTURAL SPARSITY IN neural image compression 29

EXPLORING STRUCTURAL SPARSITY IN NEURAL IMAGE COMPRESSION

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IEEE International Conference on image Processing (ICIP)

作者： Yin, Shanzhi Li, Chao Meng, Fanyang Tan, Wen Bao, Youneng Liang, Yongsheng Liu, Wei Harbin Inst Technol Shenzhen Peoples R China PengCheng Lab Shenzhen Peoples R China Shenzhen Inst Informat Technol Shenzhen Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

The performance of neural image compression have reached or suppressed traditional methods (such as JPEG, BPG, WebP). However, their sophisticated network structures with cascaded convolution layers bring heavy computational burden for practical deployment. In this paper, we explore structural sparsity in neural image compression network to obtain real-time acceleration without any specialized hardware design or algorithm. We propose a simple plug-in adaptive binary channel masking(ABCM) to judge the importance of each convolution channel and introduce sparsity during training. During inference, the unimportant channels are pruned to obtain slimmer network and less computation. We implement our method into three neural image compression networks with different entropy models to verify its effectiveness and generalization, the experiment results show that up to 7x computation reduction and 3x acceleration can be achieved with negligible performance drop.

关键词： neural image compression structural sparsity channel pruning

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Asymmetric neural image compression with High-Preserving Information

Asymmetric Neural Image Compression with High-Preserving Inf...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Hui, Kai Liu, Yu Xu, Fang Liu, Renhe Wang, Han Song, Shenghui Tianjin Univ Sch Microelect Tianjin Peoples R China Southwest Univ Chongqing Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798350330991;9798350331004

Recently, neural image compression has made significant progress in reducing rate-distortion and has received widespread attention. However, existing methods focus more on perfecting entropy models yet overlook the ability of their encoder networks to extract non-linear features of images, which can promote compression performance. In this paper, we design a learning-based asymmetric image compression network to enhance the feature representation capability for improved compression quality. Firstly, we propose a high-preserving information block (HPIB) consisting of a high-frequency filtering module (HFM) and a feature modulation module (FMM) to fully utilize the different frequency information in images. Secondly, we progressively use the HPIB layer to design a high-performance encoder network for high-fidelity feature extraction. Results from extensive experiments demonstrate that our network performs superior to the prior art in terms of both PSNR and MS-SSIM metrics and achieves 3.91% and 8.88 % BD-rate over VVC on the Kodak and CLIC datasets, respectively.

关键词： neural image compression Encoder Networks High-preserving Information Block

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GPU-Intensive Fast Entropy Coding Framework for neural image compression

GPU-Intensive Fast Entropy Coding Framework for Neural Image...

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IEEE International Conference on Visual Communications and image Processing (VCIP) - Visual Communications in the Era of AI and Limited Resources

作者： Akutsu, Hiroaki Naruko, Takahiro Suzuki, Akifumi Hitachi Ltd Res & Dev Grp Tokyo Japan

ISBN: (纸本)9781728185514

neural compression has benefited from technological advances such as convolutional neural networks (CNNs) to achieve advanced bitrates, especially in image compression. In neural image compression, an encoder and a decoder can run in parallel on a GPU, so the speed is relatively fast. However, the conventional entropy coding for neural image compression requires serialized iterations in which the probability distribution is estimated by multi-layer CNNs and entropy coding is processed on a CPU. Therefore, the total compression and decompression speed is slow. We propose a fast, practical, GPU-intensive entropy coding framework that consistently executes entropy coding on a GPU through highly parallelized tensor operations, as well as an encoder, decoder, and entropy estimator with an improved network architecture. We experimentally evaluated the speed and rate-distortion performance of the proposed framework and found that we could significantly increase the speed while maintaining the bitrate advantage of neural image compression.

关键词： neural image compression entropy coding GPU

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RQAT-INR: Improved Implicit neural image compression

RQAT-INR: Improved Implicit Neural Image Compression

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Data compression Conference (DCC)

作者： Damodaran, Bharath Bhushan Balcilar, Muhammet Galpin, Franck Hellier, Pierre InterDigital Inc Rennes France

ISBN: (纸本)9798350347951

Deep variational autoencoders for image and video compression have gained significant attraction in the recent years, due to their potential to offer competitive or better compression rates compared to the decades long traditional codecs such as AVC, HEVC or VVC. However, because of complexity and energy consumption, these approaches are still far away from practical usage in industry. More recently, implicit neural representation (INR) based codecs have emerged, and have lower complexity and energy usage to classical approaches at decoding. However, their performances are not in par at the moment with state-of-the-art methods. In this research, we first show that INR based image codec has a lower complexity than VAE based approaches, then we propose several improvements for INR-based image codec and outperformed baseline model by a large margin.

关键词： implicit neural image compression implicit neural representation learning based compression low complexity neural image compression neural image compression quantization aware training

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Content Adaptive Latents and Decoder for neural image compression 17th

Content Adaptive Latents and Decoder for Neural Image Compre...

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17th European Conference on Computer Vision (ECCV)

作者： Pan, Guanbo Lu, Guo Hu, Zhihao Xu, Dong Beihang Univ Sch Software Beijing Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9783031197963;9783031197970

In recent years, neural image compression (NIC) algorithms have shown powerful coding performance. However, most of them are not adaptive to the image content. Although several content adaptive methods have been proposed by updating the encoder-side components, the adaptability of both latents and the decoder is not well exploited. In this work, we propose a new NIC framework that improves the content adaptability on both latents and the decoder. Specifically, to remove redundancy in the latents, our content adaptive channel dropping (CACD) method automatically selects the optimal quality levels for the latents spatially and drops the redundant channels. Additionally, we propose the content adaptive feature transformation (CAFT) method to improve decoder-side content adaptability by extracting the characteristic information of the image content, which is then used to transform the features in the decoder side. Experimental results demonstrate that our proposed methods with the encoder-side updating algorithm achieve the state-ofthe-art performance.

关键词： neural image compression Content adaptive coding

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