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scalable image coding Based on Epitomes

IEEE TRANSACTIONS ON image PROCESSING 2017年第8期26卷 3624-3635页

作者： Alain, Martin Guillemot, Christine Thoreau, Dominique Guillotel, Philippe INRIA F-35042 Rennes France Technicolor Res & Innovat Dept F-35576 Cesson Sevigne France

In this paper, we propose a novel scheme for scalable image coding based on the concept of epitome. An epitome can be seen as a factorized representation of an image. Focusing on spatial scalability, the enhancement layer of the proposed scheme contains only the epitome of the input image. The pixels of the enhancement layer not contained in the epitome are then restored using two approaches inspired from local learning-based super-resolution methods. In the first method, a locally linear embedding model is learned on base layer patches and then applied to the corresponding epitome patches to reconstruct the enhancement layer. The second approach learns linear mappings between pairs of co-located base layer and epitome patches. Experiments have shown that the significant improvement of the rate-distortion performances can be achieved compared with the scalable extension of HEVC (SHVC).

关键词： Epitome super-resolution scalable image coding SHVC

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scalable image coding for Humans and Machines Using Feature Fusion Network 26

Scalable Image Coding for Humans and Machines Using Feature ...

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26th International Workshop on Multimedia Signal Processing

作者： Shindo, Takahiro Watanabe, Taiju Tatsumi, Yui Watanabe, Hiroshi Waseda Univ Tokyo Japan

ISBN: (纸本)9798350387261;9798350387254

As image recognition models become more prevalent, scalable coding methods for machines and humans gain more importance. Applications of image recognition models include traffic monitoring and farm management. In these use cases, the scalable coding method proves effective because the tasks require occasional image checking by humans. Existing image compression methods for humans and machines meet these requirements to some extent. However, these compression methods are effective solely for specific image recognition models. We propose a learning-based scalable image coding method for humans and machines that is compatible with numerous image recognition models. We combine an image compression model for machines with a compression model, providing additional information to facilitate image decoding for humans. The features in these compression models are fused using a feature fusion network to achieve efficient image compression. Our method's additional information compression model is adjusted to reduce the number of parameters by enabling combinations of features of different sizes in the feature fusion network. Our approach confirms that the feature fusion network efficiently combines image compression models while reducing the number of parameters. Furthermore, we demonstrate the effectiveness of the proposed scalable coding method by evaluating the image compression performance in terms of decoded image quality and bitrate. Code is available at https://***/final-0/ICM-v1.

关键词： scalable image coding image coding for Machines Learned image Compression

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High performance scalable image coding

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Journal of Systems Engineering and Electronics 2007年第4期18卷 795-800页

作者： Gan Tao He Yanmin Zhu Weile chool of Electronic Engineering Univ. of Electronic Science and Technology of China Chengdu 610054 P. R. China

A high performance scalable image coding algorithm is proposed. The salient features of this algorithm are the ways to form and locate the significant clusters. Thanks to the list structure, the new coding algorithm achieves fine fractional bit-plane coding with negligible additional complexity. Experiments show that it performs comparably or better than the state-of-the-art coders. Furthermore, the flexible codec supports both quality and resolution scalability, which is very attractive in many network applications.

关键词： scalable image coding significant clusters list structure

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Resolution scalable image coding With Reversible Cellular Automata

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IEEE TRANSACTIONS ON image PROCESSING 2011年第5期20卷 1461-1468页

作者： Cappellari, Lorenzo Milani, Simone Cruz-Reyes, Carlos Calvagno, Giancarlo Univ Padua Dept Informat Engn I-35131 Padua Italy Univ Rovira & Virgili Res Grp Math Linguist Tarragona 43002 Spain

In a resolution scalable image coding algorithm, a multiresolution representation of the data is often obtained using a linear filter bank. Reversible cellular automata have been recently proposed as simpler, nonlinear filter banks that produce a similar representation. The original image is decomposed into four subbands, such that one of them retains most of the features of the original image at a reduced scale. In this paper, we discuss the utilization of reversible cellular automata and arithmetic coding for scalable compression of binary and grayscale images. In the binary case, the proposed algorithm that uses simple local rules compares well with the JBIG compression standard, in particular for images where the foreground is made of a simple connected region. For complex images, more efficient local rules based upon the lifting principle have been designed. They provide compression performances very close to or even better than JBIG, depending upon the image characteristics. In the grayscale case, and in particular for smooth images such as depth maps, the proposed algorithm outperforms both the JBIG and the JPEG2000 standards under most coding conditions.

关键词： Arithmetic coding cellular automata (CA) scalable image coding

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STSIC: Swin-transformer-based scalable image coding for human and machine

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JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2024年 98卷

作者： Wang, Shipei An, Ping Yang, Chao Huang, Kunqiang Huang, Xinpeng Shanghai Univ Sch Commun & Informat Sci Key Lab Specialty Fiber Opt & Opt Access Networks Shanghai 200444 Peoples R China

Recently, as the visual data captured by smart terminal devices is mainly served for machine analysis, e.g., image classification, object detection, and sometimes also for human viewing in special scenarios, a new compression framework should be proposed to meet this demand. In this work, an image coding framework for machine analysis and human viewing simultaneously is proposed, termed STSIC. In order to leverage the relationship between the two tasks, STSIC is built upon a scalable structure, comprising a base layer for machine analysis and image preview at low quality, as well as an enhancement layer for high-quality image reconstruction. More specifically, features are extracted through the backbone and transmitted to the cloud side for tasks on the base layer. Different from the vast majority of existing works, we try to employ the Swin-Transformer as the backbone instead of a convolutional neural network (CNN). Additionally, due to the hierarchical structure of the Swin-Transformer, a feature fusion strategy is applied to generate a common feature that is more friendly to both human and machine tasks. The enhancement layer transfers the residuals between the original images and the reconstructed images from the base layer, thereby improving the quality of image reconstruction for human viewing. The experimental results show that STSIC outperforms relevant benchmarks on object detection, especially under low bitrates. As for image reconstruction, the advantage is weakened, but it still surpasses HEVC on the rate-distortion performance.

关键词： scalable image coding Multi -task network Swin-transformer Video coding for machine

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Aliasing reduction via frequency roll-off for scalable image/video coding

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2008年第1期18卷 48-58页

作者： Wu, Yongjun Woods, John W. Microsoft Corp Redmond WA 98052 USA Rensselaer Polytech Inst Dept Elect Comp & Syst Engn Troy NY 12180 USA

The extracted low-resolution video from a motion compensated 3-D subband/wavelet scalable video coder is unnecessarily sharp and sometimes contains significant aliasing, compared to that obtained by the MPEG4 lowpass filter. In this paper. we propose a content adaptive method for aliasing reduction in subband/wavelet scalable image and video coding. We try to make the low-resolution frame (LL subband) visually and energy-wise similar to that of the MPEG4 decimation filter through frequency roll-off. Scaling of the subbands is introduced to make the variances of the subbands comparable in these two cases. Thanks to the embedded properties of the EZBC coder, we can achieve the needed scaling of energies in each subband by subbitplane shift in the extractor and value(coefficient) scaling in the decoder. Two methods are presented which offer substantial peak signal-to-noise ratio (PSNR) gain for lower spatial resolution, as well as substantial reduction in visible aliasing, with little or no reduction in full resolution PSNR.

关键词： aliasing reduction frequency roll-off motion compensated temporal filtering scalable image coding scalable video coding

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Rate-distortion based scalable progressive image coding

Rate-distortion based scalable progressive image coding

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Conference on Mathematics of Data/image coding, Compression, and Encryption

作者： Carey, WK Von Pischke, LA Hemami, SS Cornell Univ Sch Elect Engn Ithaca NY 14850 USA

ISBN: (纸本)0819429112

The emergence of distributed, heterogeneous media such as the Internet has established the practical importance of progressive image transmission, in which an image is transmitted in such a way as to admit coarse rendering and recognition at the decoder as early as possible in the bitstream. This paper presents a wavelet-based progressive image transmission algorithm that attempts to achieve several goals not addressed by other image compression algorithms in the literature. First, the algorithm evaluates the tradeoff between rate and distortion as a criterion for selecting wavelet coefficients for transmission. The distortion metric is not limited to mean squared error;the algorithm provides a framework for investigating any distortion function including psychovisual and segmentation-based distortion metrics. Second, it provides a high degree of spatial scalability by sending coarser resolution information earlier in the bitstream than detail information and does not waste bits by refining high frequency subbands early. Finally, the algorithm is computationally asymmetric, pairing a very fast decoder with an encoder that can be as computationally intensive as required. The performance of the algorithm is comparable with current coders at low bitrates.

关键词： progressive image coding scalable image coding wavelets deterministic rate-distortion

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scalable Resolution-based image coding Algorithm

Scalable Resolution-based Image Coding Algorithm

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11th International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Claiboon, Phakphoom Wongthanavasu, Sartra Khon Kaen Univ Fac Sci Dept Comp Sci Cellular Automata & Knowledge Engn CAKE LabMachi Khon Kaen 40002 Thailand

ISBN: (纸本)9781479958221

Currently, multimedia data has become increasingly more important with the growth of the Internet. images are one example of sharing across the internet, especially through social media applications. More specifically, nearly every website consists of a considerable amount of image data. Some pictures are stored in multiple resolutions to be displayed as desired. When the same image is stored in different resolutions, it is difficult to manage. This paper presents an algorithm for image coding in scalable resolution. The proposed method consists of an intra-prediction and reversible cellular automata. Intra-predication aims to reduce the data to be compressed and reversible cellular automata allows image data to increase or decrease to the desired resolution. For the performance evaluation, an 8-bit grayscale was experimented with using the proposed method and then compared with JPEG2000 and RCAGIC using PSNR and SSIM statistics. In this regard, the proposed method provides more promising results than RCAGIC and JPEG2000.

关键词： component scalable image coding Intra-prediction Cellular automata (CA)

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Modifying integer wavelet transforms for scalable near-lossless image coding

Modifying integer wavelet transforms for scalable near-lossl...

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Conference on Visual Communications and image Processing 2003

作者： Charith, G Abhayaratne, K CWI Ctr Math & Comp Sci NL-1090 GB Amsterdam Netherlands

ISBN: (纸本)0819450235

In near-lossless image coding, each reconstructed pixel of the decoded image differs from the corresponding one in the original image by not more than a pre-specified value delta. Such schemes are mainly based on predictive coding techniques, which are not capable of scalable decoding. Lossless image coding with scalable decoding is mainly based on integer wavelet transforms. In this paper, methods to modify integer wavelet transforms for near-lossless image coding with scalable decoding features are presented. This is achieved by incorporating the near-lossless quantisation process, driven by 6, into lifting steps (online quantisation). Two online quantisation techniques based on 1-D and 2-D transforms are presented. They outperform the pre-quantisation based near-lossless image coding method in both bit rate and rms error performances. Further, they result in both subjectively and objectively superior performance in spatial and bit rate scalable decoding. The 2-D online scheme results in comparable performance with JPEG-LS, which is not capable of scalable decoding. It is evident from this research that with these novel schemes, scalable decoding features can be integrated into near-lossless coding with only a small increase in bit rate compared to those achieved in JPEG-LS.

关键词： near-lossless image coding online quantisation scalable image coding pre-quantisation lifting integer wavelet transforms JPEG-LS

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scalable Resolution-based image coding Algorithm

Scalable Resolution-based Image Coding Algorithm

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International Joint Conference on Computer Science and Software Engineering

作者： Phakphoom Claiboon Sartra Wongthanavasu Cellular Automata and Knowledge Engineering (CAKE) Laboratory Machine Learning and Intelligent Systems (MLIS) Laboratory Department of Computer Science Faculty of Science Khon Kaen University

ISBN: (纸本)9781479958238

关键词： Component scalable image coding Intra-prediction Cellular automata (CA)

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