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Two-stage Parallax Correction and Multi-stage Cross-view Fusion Network Based Stereo image Super-Resolution

Two-stage Parallax Correction and Multi-stage Cross-view Fus...

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

作者： Zheng, Yijian Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

Stereo image super-resolution (SR) has achieved great progress in recent years. However, the two major problems of the existing methods are that the parallax correction is insufficient and the cross-view information fusion only occurs in the beginning of the network. To address these problems, we propose a two-stage parallax correction and a multi-stage cross-view fusion network for better stereo image SR results. Specially, the two-stage parallax correction module consists of horizontal parallax correction and refined parallax correction. The first stage corrects horizontal parallax by parallax attention. The second stage is based on deformable convolution to refine horizontal parallax and correct vertical parallax simultaneously. Then, multiple cascaded enhanced residual spatial feature transform blocks are developed to fuse cross-view information at multiple stages. Extensive experiments show that our method achieves state-of-the-art performance on the KITTI2012, KITTI2015, Middlebury and Flickr1024 datasets.

关键词： Stereo image super-resolution Parallax correction Multi-stage cross-view fusion

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Learning from the NN-based Compressed Domain with Deep Feature Reconstruction Loss

Learning from the NN-based Compressed Domain with Deep Featu...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Chen, Liuhong Sun, Heming Zeng, Xiaoyang Fan, Yibo Fudan Univ Shanghai Peoples R China Waseda Univ Tokyo Japan JST PRESTO Saitama Japan

ISBN: (纸本)9781665475921

To speedup the image classification process which conventionally takes the reconstructed images as input, compressed domain methods choose to use the compressed images without decompression as input. Correspondingly, there will be a certain decline about the accuracy. Our goal in this paper is to raise the accuracy of compressed domain classification method using compressed images output by the NN-based image compression networks. Firstly, we design a hybrid objective loss function which contains the reconstruction loss of deep feature map. Secondly, one image reconstruction layer is integrated into the image classification network for up-sampling the compressed representation. These methods greatly help increase the compressed domain image classification accuracy and need no extra computational complexity. Experimental results on the benchmark imageNet prove that our design outperforms the latest work ResNet-41 with a large accuracy gain, about 4.49% on the top-1 classification accuracy. Besides, the accuracy lagging behinds the method using reconstructed images is also reduced to 0.47%. Moreover, our designed classification network has the lowest computational complexity and model complexity.

关键词： Compressed domain image analysis image classification LIC (Learned image Compression) feature reconstruction

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Lookup Register-Tables with Interpolation for Effective image Transformation on x86/64 CPUs

Lookup Register-Tables with Interpolation for Effective Imag...

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2024 conference on visual communications and image processing

作者： Kamei, Hirokazu Honda, Soichiro Hayashi, Kohei Maeda, Yoshihiro Fukushima, Norishige Nagoya Inst Technol Grad Sch Engn Nagoya Aichi Japan Shibaura Inst Technol Coll Engn Tokyo Japan

ISBN: (纸本)9798331529543;9798331529550

Lookup tables (LUTs) are commonly used to speed up image processing by handling complex mathematical functions like sine and exponential calculations. They are used in various applications such as camera image processing, high-dynamic range imaging, and edge-preserving filtering. However, due to the increasing gap between computing and input/output performance, LUTs are becoming less effective. Even though specific circuits like SIMD can improve LUT efficiency, they still need to bridge the performance gap fully. The gap makes it difficult to choose between direct numerical and LUT calculations. For this problem, a register-LUTs method with the nearest neighbor was proposed;however, it is limited for functions with narrow-range values approaching zero. In this paper, we propose a method for using register LUTs to process images efficiently over a wide range of values. Our contributions include proposing register-LUT with linear interpolation for efficient computation, using a smaller data type for further efficiency, and suggesting an efficient data retrieving method.

关键词： SIMD register-LUT interpolation x86/64 CPU image processing

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Improving Latent Quantization of Learned image Compression with Gradient Scaling

Improving Latent Quantization of Learned Image Compression w...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Sun, Heming Yu, Lu Katto, Jiro Waseda Univ Waseda Res Inst Sci & Engn Tokyo Japan Zhejiang Univ Inst Informat & Commun Engn Hangzhou Peoples R China JST PRESTO 4-1-8 Honcho Kawaguchi Saitama Japan Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan

ISBN: (纸本)9781665475921

Learned image compression (LIC) has shown its superior compression ability. Quantization is an inevitable stage to generate quantized latent for the entropy coding. To solve the non-differentiable problem of quantization in the training phase, many differentiable approximated quantization methods have been proposed. However, the derivative of quantized latent to non-quantized latent are set as one in most of the previous methods. As a result, the quantization error between non-quantized and quantized latent is not taken into consideration in the gradient descent. To address this issue, we exploit the gradient scaling method to scale the gradient of non-quantized latent in the back-propagation. The experimental results show that we can outperform the recent LIC quantization methods.

关键词： Learned image compression Quantization Gradient scaling

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Adaptive compression of compound images

Adaptive compression of compound images

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Joint Meeting of the 14th International Workshop on Systems, Signals and image processing/EURASIP conference Focused on Speech and image processing, Multimedia communications and Services

作者： Kountchev, Roumen Milanova, Mariofanna Todorov, Vladimir Kountcheva, Roumiana Tech Univ Sofia Radiocommun Dept Bul Kl Ohridsky 8 Sofia 1000 Bulgaria UALR Dept Comp Sci Little Rock AR 72204 USA T & K Engn Sofia BG-1712 Bulgaria

ISBN: (纸本)9789612480363

In the paper is presented new method for efficient compression of compound still images, containing pictures and texts/graphics. The method is based on the Inverse Difference Pyramid (IDP) image decomposition and lossless coding of the obtained data. The method permits the recognition of texts and graphics in compound images, the setting of corresponding regions of interest (ROI) and their coding with the most efficient tools. The method ensures easy access and transfer of visual information via Internet aimed at distance learning applications.

关键词： lossless and lossy image compression image histogram analysis adaptive compression

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Perceptual image Compression With Conditional Diffusion Transformers

Perceptual Image Compression With Conditional Diffusion Tran...

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2024 conference on visual communications and image processing

作者： Mao, Rui Feng, Xinmin Gao, Changsheng Li, Li Liu, Dong Sun, Xiaoyan Univ Sci & Technol China MOE Key Lab Brain Inspired Intelligent Percept & Hefei Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Generative models have significantly advanced generative AI, particularly in image and video generation. Recognizing their potential, researchers have begun exploring their application in image compression. However, existing methods face two primary challenges: limited performance improvement and high model complexity. In this paper, to address these two challenges, we propose a perceptual image compression solution by introducing a conditional diffusion model. Given that compression performance heavily depends on the decoder's generative capability, we base our decoder on the diffusion transformer architecture. To address the model complexity problem, we implement the diffusion transformer architecture with Swin transformer. Equipped with enhanced generative capability, we further augment the decoder with informative features using a multi-scale feature fusion module. Experimental results demonstrate that our approach surpasses existing perceptual image compression methods while achieving lower model complexity.

关键词： Diffusion transformer (DiT) image generation perceptual image compression Swin transformer

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DCTResNet: Transform Domain image Deblocking for Motion Blur images

DCTResNet: Transform Domain Image Deblocking for Motion Blur...

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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： Maharjan, Paras Xu, Ning Xu, Xuan Song, Yuyan Li, Zhu Univ Missouri Kansas City MO 64110 USA Kwai Inc Palo Alto CA USA

ISBN: (纸本)9781728185514

Pixel recovery with deep learning has shown to be very effective for a variety of low-level vision tasks like image super-resolution, denoising, and deblurring. Most existing works operate in the spatial domain, and there are few works that exploit the transform domain for image restoration tasks. In this paper, we present a transform domain approach for image deblocking using a deep neural network called DCTResNet. Our application is compressed video motion deblur, where the input video frame has blocking artifacts that make the deblurring task very challenging. Specifically, we use a block-wise Discrete Cosine Transform (DCT) to decompose the image into its low and high-frequency sub-band images and exploit the strong subband specific features for more effective deblocking solutions. Since JPEG also uses DCT for image compression, using DCT sub-band images for image deblocking helps to learn the JPEG compression prior to effectively correct the blocking artifacts. Our experimental results show that both PSNR and SSIM for DCTResNet perform more favorably than other state-of-the-art (SOTA) methods, while significantly faster in inference time.

关键词： Discrete cosine transforms

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An efficient JPE62000 tier-1 coder hardware implementation for real-time video processing

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2003年第4期49卷 780-786页

作者： Schumacher, PR Xilinx Inc Xilinx Res Labs Longmont CO 80503 USA

The recently approved digital still image standard known as JPEG2000 promises to be an excellent image and video format for use with a large range of applications. For adoption of the standard to take place in the consumer marketplace, implementations supporting real-time encoding and decoding of popular image and video formats must be created. It is a well-known fact that the major bottleneck of a JPEG2000 system is the bit/context modeling and arithmetic coding tasks (also known as tier-1 coding). This paper discusses a hardware implementation of a tier-1 coder that exploits available parallelisms. The proposed technique described in this paper is approximately 50% faster than the best technique described in the literature(1).

关键词： field programmable gate arrays image processing JPEG2000 video signal processing

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Quality enhancement of printed-and-scanned images using distributed coding

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SIGNAL processing 2007年第6期87卷 1301-1313页

作者： Voloshynovskiy, S. Koval, O. Deguillaume, F. Pun, T. Univ Geneva CUI CH-1211 Geneva 4 Switzerland

In this paper we address visual communications via print-and-scan channels from an information-theoretic point of view as communications with side information that targets quality enhancement of visual data at the output of this type of channels. The solution to this problem addresses important aspects of multimedia data processing and management. A practical approach to side information communications for printed documents based on Wyner-Ziv and Gray setups is analyzed in the paper that assumes two separated communications channels where an appropriate distributed coding should be elaborated. The printing channel is considered to be a direct visual channel for images ("analog" channel with degradations). The "digital channel" is considered to be an appropriate auxiliary channel exploited to communicate the information exploited for quality enhancement of printed-and-scanned image. We demonstrate both theoretically and practically how one can benefit from this sort of "distributed paper communications". (c) 2006 Elsevier B.V. All rights reserved.

关键词： data-hiding printed-and-scanned documents communications with side-information bar codes distributed coding joint source-channel coding Gel'fand-Pinsker problem Wyner-Ziv coding gray coding image enhancement

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Frequency-aware Learned image Compression for Quality Scalability

Frequency-aware Learned Image Compression for Quality Scalab...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Choi, Hyomin Racape, Fabien Hamidi-Rad, Shahab Ulhaq, Mateen Feltman, Simon Interdigital Emerging Technol Lab Los Altos CA 94022 USA Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

ISBN: (纸本)9781665475921

Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches. We propose a novel NN-based image coding framework that utilizes forward wavelet transforms to decompose the input signal by spatial frequency. Our encoder generates separate bitstreams for each latent representation of low and high frequencies. This enables our decoder to selectively decode bitstreams in a quality-scalable manner. Hence, the decoder can produce an enhanced image by using an enhancement bitstream in addition to the base bitstream. Furthermore, our method is able to enhance only a specific region of interest (ROI) by using a corresponding part of the enhancement latent representation. Our experiments demonstrate that the proposed method shows competitive rate-distortion performance compared to several non-scalable image codecs. We also showcase the effectiveness of our two-level quality scalability, as well as its practicality in ROI quality enhancement.

关键词： End-to-end compression learned image compression quality scalability wavelet decomposition

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