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DEPTH FROM DEFOCUS AND BLUR FOR SINGLE image

DEPTH FROM DEFOCUS AND BLUR FOR SINGLE IMAGE

引用

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

作者： Sun, Huadong Zhao, Zhijie Jin, Xuesong Niu, Lianding Zhang, Lizhi Harbin Univ Commerce Sch Comp & Informat Engn Harbin Peoples R China

ISBN: (纸本)9781479902880

Depth for single image is a hot problem in computer vision, which is very important to 2D/3D image conversion. Generally, depth of the object in the scene varies with the amount of blur in the defocus images. So, depth in the scene can be recovered by measuring the blur. In this paper, a new method for depth estimation based on focus/defocus cue is presented, where the entropy of high frequency subband of wavelet decomposition is regarded as the measure of blur. The proposed method, which is unnecessary to select threshold, can provide pixel-level depth map. The experimental results show that this method is effective and reliable.

关键词： Depth focus/defocus cue measure of blur entropy of high frequency subband

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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

2024 conference on visual communications and image processing

作者： Jia, Panqi Mao, Jue Koyuncu, Esin Koyuncu, A. Burakhan Solovyev, Timofey Karabutov, Alexander Zhao, Yin Alshina, Elena Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Nurnberg Germany Tech Univ Munich Chair Media Technol Munich Germany Huawei Technol Shenzhen Peoples R China

ISBN: (纸本)9798331529543;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 hand-crafted 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： Learned image Compression JPEG-AI bit rate matching algorithm optimization

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image Retrieval Method using visual Query Suggestion and Relevance Feedback

Image Retrieval Method using Visual Query Suggestion and Rel...

引用

4th International conference on Wireless communications and Signal processing (WCSP)

作者： Zhang, Jing Yang, Yuncong Zhuo, Li Diao, Mengmeng Beijing Univ Technol Signal & Informat Proc Lab Beijing Peoples R China

ISBN: (纸本)9781467358293;9781467358309

Query suggestion is an effective human-computer interaction (HCI) approach in information retrieval. According to human vision system, the image retrieval method using visual query suggestion (VQS) can provide a friendly query interface to solve the query ambiguity problem. In this paper, the contentbased image retrieval (CBIR) system is realized first. Human-computer interaction used VQS to obtain users' intention. In this step, user submitted a query keyword to the system. VQS is utilized to provide a list of suggestions, each containing a keyword and a collection of representative images. If the user selects one of the image suggestions, this image will be viewed as the key image. Then CBIR system will retrieve the image sets to return the similar images based on the similarity of image features. In relevance feedback, user scored each returned image by the slider to optimize retrieval results. A friendly query interface is designed to carry out HCI in our system and the experimental result shows the proposed method can improve the average recall and precision efficiently.

关键词： human-computer interaction visual query suggestion content-based image retrieval relevance feedback

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Optimization of Probability Distributions for Residual Coding of Screen Content

Optimization of Probability Distributions for Residual Codin...

引用

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

作者： Och, Hannah Strutz, Tilo Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg FAU Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany Leipzig Univ Telecommun Deutsch Telekom AG Inst Commun Engn Gustav Freytag Str 43-45 D-04277 Leipzig Germany

ISBN: (纸本)9781728185514

Probability distribution modeling is the basis for most competitive methods for lossless coding of screen content. One such state-of-the-art method is known as soft context formation (SCF). For each pixel to be encoded, a probability distribution is estimated based on the neighboring pattern and the occurrence of that pattern in the already encoded image. Using an arithmetic coder, the pixel color can thus be encoded very efficiently, provided that the current color has been observed before in association with a similar pattern. If this is not the case, the color is instead encoded using a color palette or, if it is still unknown, via residual coding. Both palette-based coding and residual coding have significantly worse compression efficiency than coding based on soft context formation. In this paper, the residual coding stage is improved by adaptively trimming the probability distributions for the residual error. Furthermore, an enhanced probability modeling for indicating a new color depending on the occurrence of new colors in the neighborhood is proposed. These modifications result in a bitrate reduction of up to 2.9 % on average. Compared to HEVC (HM-16.21 + SCM-8.8) and FLIF, the improved SCF method saves on average about 11 % and 18 % rate, respectively.

关键词： distribution modelling lossless coding screen content coding color image compression soft context formation

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Volumetric End-to-End Optimized Compression for Brain images

Volumetric End-to-End Optimized Compression for Brain Images

引用

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

作者： Gao, Shuo Zhang, Yueyi Liu, Dong Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728180687

The amount of volumetric brain image increases rapidly, which requires a vast amount of resources for storage and transmission, so it's urgent to explore an efficient volumetric compression method. Recent years have witnessed the progress of deep learning-based approaches for two-dimensional (2D) natural image compression, but the field of learned volumetric image compression still remains unexplored. In this paper, we propose the first end-to-end learning framework for volumetric image compression by extending the advanced techniques of 2D image compression to volumetric images. Specifically, a convolutional autoencoder is used to compress 3D image cubes, and the non-local attention models are embedded in the convolutional autoencoder to jointly capture local and global correlations. Both hyperprior and autoregressive models are used to perform the conditional probability estimation in entropy coding. To reduce model complexity, we introduce a convolutional long short-term memory network for the autoregressive model based on channel-wise prediction. Experimental results on volumetric mouse brain images show that the proposed method outperforms JPEG2000-3D, HEVC and state-of-the-art 2D methods.

关键词： Volumetric image compression 3D convolutional neural network convolutional long short-term memory

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MULTI-MODEL PREDICTION FOR image SET COMPRESSION

MULTI-MODEL PREDICTION FOR IMAGE SET COMPRESSION

引用

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

作者： Shi, Zhongbo Sun, Xiaoyan Wu, Feng Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781479902880

The key task in image set compression is how to efficiently remove set redundancy among images and within a single image. In this paper, we propose the first multi-model prediction (MoP) method for image set compression to significantly reduce inter image redundancy. Unlike the previous prediction methods, our MoP enhances the correlation between images using feature-based geometric multi-model fitting. Based on estimated geometric models, multiple deformed prediction images are generated to reduce geometric distortions in different image regions. The block-based adaptive motion compensation is then adopted to further eliminate local variances. Experimental results demonstrate the advantage of our approach, especially for images with complicated scenes and geometric relationships.

关键词： image set compression multiple models prediction HEVC

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A Hybrid Model for Natural Face De-Identiation with Adjustable Privacy

A Hybrid Model for Natural Face De-Identiation with Adjustab...

引用

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

作者： Wen, Yunqian Liu, Bo Xie, Rong Zhu, Yunhui 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)9781728180687

As more and more personal photos are shared and tagged in social media, security and privacy protection are becoming an unprecedentedly focus of attention. Avoiding privacy risks such as unintended verification, b ecomes increasingly challenging. To enable people to enjoy uploading photos without having to consider these privacy concerns, it is crucial to study techniques that allow individuals to limit the identity information leaked in visual data. In this paper, we propose a novel hybrid model consists of two stages to generate visually pleasing deidentified f ace i mages a ccording t o a s ingle i nput. Meanwhile, we successfully preserve visual similarity with the original face to retain data usability. Our approach combines latest advances in GAN-based face generation with well-designed adjustable randomness. In our experiments we show visually pleasing deidentified output of our method while preserving a high similarity to the original image content. Moreover, our method adapts well to the verificator of unknown structure, which further improves the practical value in our real life.

关键词： privacy protection multi-level features fusion GAN-based face generation randomness

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On regularized dequantizers for DCT-based transform coding of images

On regularized dequantizers for DCT-based transform coding o...

引用

conference on visual communications and image processing 2001

作者： Lee, G Kim, S Song, SMH Korea Univ Sch Elect Engn Sungbuk Gu Seoul 136701 South Korea

ISBN: (纸本)0819439886

We have recently proposed a new dequantization scheme for DCT-based transform coding based on regularization principles [1]. The new approach sharply reduced blocking artifacts in decoded images and the performance of the new dequantization scheme has been evaluated against the standard JPEG, MPEG and H.263+ in terms of the peak-signal-to-noise ratio (PSNR) with our own definition of the blockiness measure (BM). Basically, the proposed dequantizer maps the received data to within the range +/- (quantizer spacing/2), so that the final decompressed image is ''smooth" in the sense of minimizing the cost functional including the stabilizing term weighted by a regularization parameter. In this paper, we focus on several important aspect of this regularized dequantizer, namely the selection of the regularization parameter and the convergence of the dequantization algorithm.

关键词： regularization dequantization DCT MPEG H.26x image processing

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Joint compression-classification with quantizer/classifier dimension mismatch

Joint compression-classification with quantizer/classifier d...

引用

conference on visual communications and image processing 2001

作者： Srinivasamurthy, N Ortega, A Univ So Calif Dept EE Syst Integrated Media Syst Ctr Los Angeles CA 90089 USA

ISBN: (纸本)0819439886

In this paper an algorithm is presented to design, encoders that achieve good compression and classification. The goal is to minimize the classification error introduced by quantizing the data using encoders operating on low dimension, inputs, which are subsets of the high dimension data used by the classifier for classification. In the encoder design information from the other dimensions of the vector is used to develop efficient encoders which are capable of achieving lower classification error for a given distortion. The design allows a trade-off between distortion and classifications costs providing more flexibility in the overall system design. The algorithm is tested on Gaussian mixture data, which is classified using a classifier which takes as input vectors of quantized valves. Tile proposed technique can trade performance to achieve lower complexity, which is desirable in devices having limited computational resources. For 4 dimensional Gaussian mixture data the misclassification was about 2.2% more than, that achieved by using Encoders of the same dimension as the classifier, while the encoding complexity was reduced by more than a factor of 2.

关键词： image coding

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An unsupervised segmentation algorithm based on an iterative spectral dissimilarity measure for hyperspectral imagery

An unsupervised segmentation algorithm based on an iterative...

引用

conference on visual communications and image processing 2001

作者： Kwon, H Der, SZ Nasrabadi, NM USA Res Lab Adelphi MD 20783 USA

ISBN: (纸本)0819439886

We present an adaptive unsupervised segmentation technique, in which spectral features are obtained and processed without a priori knowledge of the spectral characteristics. The proposed technique is based on an iterative method, in which segmentation at a given iteration depends closely on the segmentation results at the previous iteration. The hyperspectral images are first coarsely segmented and then the segmentation is successively refined via an iterative spectral dissimilarity measure. The algorithm also provides reduced computational complexity and improved segmentation performance. The algorithm consists of (i) an initial segmentation based on a fixed spectral dissimilarity measure and the k-means algorithm, and (ii) subsequent adaptive segmentation based on an iterative spectral dissimilarity measure over a local region whose size is reduced progressively. The iterative use of a local spectral dissimilarity measure provided a set of values that can discriminate among different materials. The proposed unsupervised segmentation technique proved to be superior to other unsupervised algorithms, especially when a large number of different materials are mixed in complex hyperspectral scenes.

关键词： multispectral processing unsupervised segmentation hyperspectral imagery adaptive segmentation

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