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Analyzing Time Complexity of Practical Learned image Compression Models

Analyzing Time Complexity of Practical Learned Image Compres...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Pan, Xiaohan Guo, Zongyu Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei Peoples R China

ISBN: (纸本)9781728185514

We have witnessed the rapid development of learned image compression (LIC). The latest LIC models have outperformed almost all traditional image compression standards in terms of rate-distortion (RD) performance. However, the time complexity of LIC model is still underdiscovered, limiting the practical applications in industry. Even with the acceleration of GPU, LIC models still struggle with long coding time, especially on the decoder side. In this paper, we analyze and test a few prevailing and representative LIC models, and compare their complexity with traditional codecs including H.265/HEVC intra and H.266/VVC intra. We provide a comprehensive analysis on every module in the LIC models, and investigate how bitrate changes affect coding time. We observe that the time complexity bottleneck mainly exists in entropy coding and context modelling. Although this paper pay more attention to experimental statistics, our analysis reveals some insights for further acceleration of LIC model, such as model modification for parallel computing, model pruning and a more parallel context model.

关键词： Time Complexity Analysis image Compression Parallel Computing Neural Networks

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See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recovery from a Single Rainy image

See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recove...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Wang, Ping Wu, Wei Li, Zhu Liu, Yong Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO USA

ISBN: (纸本)9781728185514

Scale-Invariant Feature Transform (SIFT) is one of the most well-known image matching methods, which has been widely applied in various visual fields. Because of the adoption of a difference of Gaussian (DoG) pyramid and Gaussian gradient information for extrema detection and description, respectively, SIFT achieves accurate key points and thus has shown excellent matching results but except under adverse weather conditions like rain. To address the issue, in the paper we propose a divide-and-conquer SIFT key points recovery algorithm from a single rainy image. In the proposed algorithm, we do not aim to improve quality for a derained image, but divide the key point recovery problem from a rainy image into two sub-problems, one being how to recover the DoG pyramid for the derained image and the other being how to recover the gradients of derained Gaussian images at multiple scales. We also propose two separate deep learning networks with different losses and structures to recover them, respectively. This divide-and-conquer scheme to set different objectives for SIFT extrema detection and description leads to very robust performance. Experimental results show that our proposed algorithm achieves state-of-the-art performances on widely used image datasets in both quantitative and qualitative tests.

关键词： SIFT key point recovery rainy image extrema detection and description deep learning

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Learn to overfit better: finding the important parameters for learned image compression

Learn to overfit better: finding the important parameters fo...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Zhang, Honglei Cricri, Francesco Tavakoli, Hamed Rezazadegan Santamaria, Maria Lam, Yat-Hong Hannuksela, Miska M. Nokia Technol Espoo Finland

ISBN: (纸本)9781728185514

For most machine learning systems, overfitting is an undesired behavior. However, overfitting a model to a test image or a video at inference time is a favorable and effective technique to improve the coding efficiency of learning-based image and video codecs. At the encoding stage, one or more neural networks that are part of the codec are finetuned using the input image or video to achieve a better coding performance. The encoder encodes the input content into a content bitstream. If the finetuned neural network is part (also) of the decoder, the encoder signals the weight update of the finetuned model to the decoder along with the content bitstream. At the decoding stage, the decoder first updates its neural network model according to the received weight update, and then proceeds with decoding the content bitstream. Since a neural network contains a large number of parameters, compressing the weight update is critical to reducing bitrate overhead. In this paper, we propose learning-based methods to find the important parameters to be overfitted, in terms of rate-distortion performance. Based on simple distribution models for variables in the weight update, we derive two objective functions. By optimizing the proposed objective functions, the importance scores of the parameters can be calculated and the important parameters can be determined. Our experiments on lossless image compression codec show that the proposed method significantly outperforms a prior-art method where overfitted parameters were selected based on heuristics. Furthermore, our technique improved the compression performance of the state-of-the-art lossless image compression codec by 0.1 bit per pixel.

关键词： image compression

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CRC-Based Multi-Error Correction of H.265 Encoded Videos in Wireless communications

CRC-Based Multi-Error Correction of H.265 Encoded Videos in ...

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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： Boussard, Vivien Coulombe, Stephane Coudoux, Francois-Xavier Corlay, Patrick Trioux, Anthony Ecole Technol Super Dept Software & IT Engn Montreal PQ Canada Univ Lille CNRS UPHF ISENCent LilleUMR 8520IEMNDOAE Valenciennes France

ISBN: (纸本)9781728185514

This paper analyzes the benefits of extending CRC-based error correction (CRC-EC) to handle more errors in the context of error-prone wireless networks. In the literature, CRC-EC has been used to correct up to 3 binary errors per packet. We first present a theoretical analysis of the CRC-EC candidate list while increasing the number of errors considered. We then analyze the candidate list reduction resulting from subsequent checksum validation and video decoding steps. Simulations conducted on two wireless networks show that the network considered has a huge impact on CRC-EC performance. Over a Bluetooth low energy (BLE) channel with Eb/No=8 dB, an average PSNR improvement of 4.4 dB on videos is achieved when CRC-EC corrects up to 5, rather than 3 errors per packet.

关键词： Cyclic Redundancy Check Error Correction H.264 H.265 Wireless communications

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The Steganographic Method of Introducing Additional Information, Resistant to Transcoding and Conversion

The Steganographic Method of Introducing Additional Informat...

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2022 Systems of Signals Generating and processing in the Field of on Board communications, SOSG 2022

作者： Drebuzhan, A.M. Nezhivleva, K.I. Potashnikov, A.M. Vlasuyk, I.V. Mozhaeva, A.I. Moscow Technical University of Communications and Informatics Moscow Russia University of Waikato Hamilton New Zealand

ISBN: (纸本)9781665406352

In many current videos is necessary to include hidden information. It can be done with the use of steganography. Steganography is based on the limited capabilities of human senses, which is why people are not able to distinguish minor variations of colors. Based on the research of limit to human vision, here we offer a new method and test equipment for research and measure the characteristic of hiding information in the digital image. The presented steganographic method is based on Discrete Wavelet Transform (DWT) for the transition to the lower frequency domain. The main goal is to embed information in a digital image so that both the message and the fact of its presence are hidden. The presented method reflects the internal mechanisms of the visual system and is convenient for describing the process of image perception. The experimental results were compared with other methods and proved to be satisfactory © 2022 IEEE.

关键词： Discrete wavelet transforms

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Learning in Compressed Domain for Faster Machine Vision Tasks

Learning in Compressed Domain for Faster Machine Vision Task...

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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： Liu, Jinming Sun, Heming Katto, Jiro Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan Waseda Univ Waseda Res Inst Sci & Engn Tokyo Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama Japan

ISBN: (纸本)9781728185514

Learned image compression (LIC) has illustrated good ability for reconstruction quality driven tasks (e.g. PSNR, MS-SSIM) and machine vision tasks such as image understanding. However, most LIC frameworks are based on pixel domain, which requires the decoding process. In this paper, we develop a learned compressed domain framework for machine vision tasks. 1) By sending the compressed latent representation directly to the task network, the decoding computation can be eliminated to reduce the complexity. 2) By sorting the latent channels by entropy, only selective channels will be transmitted to the task network, which can reduce the bitrate. As a result, compared with the traditional pixel domain methods, we can reduce about 1/3 multiply-add operations (MACS) and 1/5 inference time while keeping the same accuracy. Moreover, proposed channel selection can contribute to at most 6.8% bitrate saving.

关键词： compressed domain image compression face alignment video coding for machine

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Detection and classification of painting defects using deep learning 36

Detection and classification of painting defects using deep ...

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36th International Technical conference on Circuits/Systems, Computers and communications (ITC-CSCC)

作者： Adachi, Kazune Natori, Takahiro Aikawa, Naoyuki Tokyo Univ Sci Tokyo Japan

ISBN: (纸本)9781665435536

In the visual inspection, the quality assurance is difficult, because the dispersion occurs in the result by skill and fatigue degree of the inspector. Recently, a visual inspection method by image processing using deep learning has been proposed. When using deep learning, the dataset to be used is important. In this paper, we describe a method for detecting painting defects using image processing, automatically generating data for deep learning, and using these data for classification using deep learning.

关键词： Painting visual inspection Deep learning image processing

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Deep Motion Flow Aided Face Video De-identification

Deep Motion Flow Aided Face Video De-identification

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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： Wen, Yunqian Liu, Bo Xie, Rong Cao, Jingyi Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781728185514

Advances in cameras and web technology have made it easy to capture and share large amounts of face videos over to an unknown audience with uncontrollable purposes. These raise increasing concerns about unwanted identity-relevant computer vision devices invading the characters's privacy. Previous de-identification methods rely on designing novel neural networks and processing face videos frame by frame, which ignore the data feature in redundancy and continuity. Besides, these techniques are incapable of well-balancing privacy and utility, and per-frame evaluation is easy to cause flicker. In this paper, we present deep motion flow, which can create remarkable de-identified face videos with a good privacy-utility tradeoff. It calculates the relative dense motion flow between every two adjacent original frames and runs the high quality image anonymization only on the first frame. The de-identified video will be obtained based on the anonymous first frame via the relative dense motion flow. Extensive experiments demonstrate the effectiveness of our proposed de-identification method.

关键词： video de-identification video anonymization privacy protection privacy-utility tradeoff

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Real-Time Implementation of Neural-Network Post-Filters for 4K 60fps VVC Videos

Real-Time Implementation of Neural-Network Post-Filters for ...

引用

IEEE visual communications and image processing (VCIP)

作者： Daichi Arai Shimpei Nemoto Kazuhisa Iguchi Atsuro Ichigaya Science & Technology Research Laboratories NHK Tokyo Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

This study investigates the practical performance of neural-network post-filters standardized in ITU-T H.274. We implement neural-network models on a Field-Programmable Gate Array (FPGA), allowing real-time processing of 4K 60fps encoded videos transmitted via 12G-SDI. Experimental results suggest that a minor bitrate increase for the transmission of the neural-network model weights can enhance the quality of the videos encoded by Versatile Video Coding (VVC).

关键词： Video coding visual communication image processing Bit rate Artificial neural networks Real-time systems Field programmable gate arrays Videos

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Feature-Guided Machine-Centric image Coding for Downstream Tasks

Feature-Guided Machine-Centric Image Coding for Downstream T...

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2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Kwak, Sangwoon Yun, Joungil Choo, Hyon-Gon Kim, Munchurl Electronics and Telecommunications Research Institute Korea Republic of Korea Advanced Institute of Science and Technology Korea Republic of

ISBN: (纸本)9798350313154

Video coding, a process of compressing and decompressing digital video content, has traditionally been optimized for human visual systems by reducing its size while maintaining the human perceptual quality. However, with the remarkable progress of artificial intelligence (AI) technology, the need for machine-centric coding has rapidly been increasing in recent years. In response to these trends, international standardization organizations such as MPEG are actively working to develop and launch new standards on coding technologies for machines, called video coding for machines (VCM). In this paper, we present a novel feature-guided block-wise image blending method for image compression, which is suitable for machine applications such as object detection and segmentation. For this, we use a gradient map of the feature loss using the pretrained encoder part of a task-specific network as guide for input degradation, so that the degraded input images can be effectively compressed for machine-centric tasks. Our method is simple but effective because additional training is not required by utilizing the pretrained encoder parts of networks for targeted tasks. Experimental results show that BD-rate gains can be obtained by applying our proposed method with averages 11% and 8% for object detection and instance segmentation tasks, respectively, compared to the image anchor results of MPEG-VCM reference software v0.4. © 2023 IEEE.

关键词： Computer vision

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