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Showing papers on "JPEG 2000 published in 2018"


Posted Content
TL;DR: It is found that in terms of compression performance, autoregressive and hierarchical priors are complementary and can be combined to exploit the probabilistic structure in the latents better than all previous learned models.
Abstract: Recent models for learned image compression are based on autoencoders, learning approximately invertible mappings from pixels to a quantized latent representation. These are combined with an entropy model, a prior on the latent representation that can be used with standard arithmetic coding algorithms to yield a compressed bitstream. Recently, hierarchical entropy models have been introduced as a way to exploit more structure in the latents than simple fully factorized priors, improving compression performance while maintaining end-to-end optimization. Inspired by the success of autoregressive priors in probabilistic generative models, we examine autoregressive, hierarchical, as well as combined priors as alternatives, weighing their costs and benefits in the context of image compression. While it is well known that autoregressive models come with a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and, together, exploit the probabilistic structure in the latents better than all previous learned models. The combined model yields state-of-the-art rate--distortion performance, providing a 15.8% average reduction in file size over the previous state-of-the-art method based on deep learning, which corresponds to a 59.8% size reduction over JPEG, more than 35% reduction compared to WebP and JPEG2000, and bitstreams 8.4% smaller than BPG, the current state-of-the-art image codec. To the best of our knowledge, our model is the first learning-based method to outperform BPG on both PSNR and MS-SSIM distortion metrics.

391 citations


Proceedings Article
03 Dec 2018
TL;DR: In this article, the authors compare the performance of autoregressive, hierarchical, and combined priors in the context of image compression and find that in terms of compression performance, autoregression and hierarchical priors are complementary and can be combined to exploit the probabilistic structure in the latents better than all previous learned models.
Abstract: Recent models for learned image compression are based on autoencoders that learn approximately invertible mappings from pixels to a quantized latent representation. The transforms are combined with an entropy model, which is a prior on the latent representation that can be used with standard arithmetic coding algorithms to generate a compressed bitstream. Recently, hierarchical entropy models were introduced as a way to exploit more structure in the latents than previous fully factorized priors, improving compression performance while maintaining end-to-end optimization. Inspired by the success of autoregressive priors in probabilistic generative models, we examine autoregressive, hierarchical, and combined priors as alternatives, weighing their costs and benefits in the context of image compression. While it is well known that autoregressive models can incur a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and can be combined to exploit the probabilistic structure in the latents better than all previous learned models. The combined model yields state-of-the-art rate–distortion performance and generates smaller files than existing methods: 15.8% rate reductions over the baseline hierarchical model and 59.8%, 35%, and 8.4% savings over JPEG, JPEG2000, and BPG, respectively. To the best of our knowledge, our model is the first learning-based method to outperform the top standard image codec (BPG) on both the PSNR and MS-SSIM distortion metrics.

355 citations


Proceedings ArticleDOI
18 Jun 2018
TL;DR: In this paper, the authors propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0), WebP, JPEG2000, and JPEG as measured by MS-SSIM.
Abstract: We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result using a single model. First, we modify the recurrent architecture to improve spatial diffusion, which allows the network to more effectively capture and propagate image information through the network's hidden state. Second, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited number of bits to encode visually complex image regions. Finally, we show that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to multiple metrics. We evaluate our method on the Kodak and Tecnick image sets and compare against standard codecs as well as recently published methods based on deep neural networks.

321 citations


Proceedings ArticleDOI
18 Jun 2018
TL;DR: Zhang et al. as mentioned in this paper proposed to adapt the bit rate of different parts of the image to local content and allocate the content-aware bit rate under the guidance of a content-weighted importance map.
Abstract: Lossy image compression is generally formulated as a joint rate-distortion optimization problem to learn encoder, quantizer, and decoder. Due to the non-differentiable quantizer and discrete entropy estimation, it is very challenging to develop a convolutional network (CNN)-based image compression system. In this paper, motivated by that the local information content is spatially variant in an image, we suggest that: (i) the bit rate of the different parts of the image is adapted to local content, and (ii) the content-aware bit rate is allocated under the guidance of a content-weighted importance map. The sum of the importance map can thus serve as a continuous alternative of discrete entropy estimation to control compression rate. The binarizer is adopted to quantize the output of encoder and a proxy function is introduced for approximating binary operation in backward propagation to make it differentiable. The encoder, decoder, binarizer and importance map can be jointly optimized in an end-to-end manner. And a convolutional entropy encoder is further presented for lossless compression of importance map and binary codes. In low bit rate image compression, experiments show that our system significantly outperforms JPEG and JPEG 2000 by structural similarity (SSIM) index, and can produce the much better visual result with sharp edges, rich textures, and fewer artifacts.

259 citations


Proceedings Article
27 Sep 2018
TL;DR: In this article, a context-adaptive entropy model is proposed for image compression, which exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bit allocation is required.
Abstract: We propose a context-adaptive entropy model for use in end-to-end optimized image compression. Our model exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bit allocation is required. Based on these contexts, we allow the model to more accurately estimate the distribution of each latent representation with a more generalized form of the approximation models, which accordingly leads to an enhanced compression performance. Based on the experimental results, the proposed method outperforms the traditional image codecs, such as BPG and JPEG2000, as well as other previous artificial-neural-network (ANN) based approaches, in terms of the peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) index.

255 citations


Journal ArticleDOI
Feng Jiang1, Wen Tao1, Shaohui Liu1, Jie Ren1, Xun Guo2, Debin Zhao1 
TL;DR: Experimental results validate that the proposed compression framework greatly outperforms several compression frameworks that use existing image coding standards with the state-of-the-art deblocking or denoising post-processing methods.
Abstract: Deep learning, e.g., convolutional neural networks (CNNs), has achieved great success in image processing and computer vision especially in high-level vision applications, such as recognition and understanding. However, it is rarely used to solve low-level vision problems such as image compression studied in this paper. Here, we move forward a step and propose a novel compression framework based on CNNs. To achieve high-quality image compression at low bit rates, two CNNs are seamlessly integrated into an end-to-end compression framework. The first CNN, named compact convolutional neural network (ComCNN), learns an optimal compact representation from an input image, which preserves the structural information and is then encoded using an image codec (e.g., JPEG, JPEG2000, or BPG). The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high quality in the decoding end. To make two CNNs effectively collaborate, we develop a unified end-to-end learning algorithm to simultaneously learn ComCNN and RecCNN, which facilitates the accurate reconstruction of the decoded image using RecCNN. Such a design also makes the proposed compression framework compatible with existing image coding standards. Experimental results validate that the proposed compression framework greatly outperforms several compression frameworks that use existing image coding standards with the state-of-the-art deblocking or denoising post-processing methods.

144 citations


Proceedings ArticleDOI
25 Apr 2018
TL;DR: Wang et al. as discussed by the authors presented a lossy image compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to achieve a high coding efficiency.
Abstract: Image compression has been investigated as a fundamental research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compression. In this paper, we present a lossy image compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to achieve a high coding efficiency. First, we design a novel CAE architecture to replace the conventional transforms and train this CAE using a rate-distortion loss function. Second, to generate a more energy-compact representation, we utilize the principal components analysis (PCA) to rotate the feature maps produced by the CAE, and then apply the quantization and entropy coder to generate the codes. Experimental results demonstrate that our method outperforms traditional image coding algorithms, by achieving a 13.7% BD-rate decrement on the Kodak database images compared to JPEG2000. Besides, our method maintains a moderate complexity similar to JPEG2000.

98 citations


Posted Content
Fabian Mentzer1, Eirikur Agustsson1, Michael Tschannen1, Radu Timofte1, Luc Van Gool1 
TL;DR: The first practical learned lossless image compression system, L3C, is proposed and it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000, and finds that learning the auxiliary representation is crucial and outperforms predefined auxiliary representations such as an RGB pyramid significantly.
Abstract: We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000. At the core of our method is a fully parallelizable hierarchical probabilistic model for adaptive entropy coding which is optimized end-to-end for the compression task. In contrast to recent autoregressive discrete probabilistic models such as PixelCNN, our method i) models the image distribution jointly with learned auxiliary representations instead of exclusively modeling the image distribution in RGB space, and ii) only requires three forward-passes to predict all pixel probabilities instead of one for each pixel. As a result, L3C obtains over two orders of magnitude speedups when sampling compared to the fastest PixelCNN variant (Multiscale-PixelCNN). Furthermore, we find that learning the auxiliary representation is crucial and outperforms predefined auxiliary representations such as an RGB pyramid significantly.

96 citations


Posted Content
TL;DR: This paper designs a novel CAE architecture to replace the conventional transforms and train this CAE using a rate-distortion loss function, and utilizes the advantages of convolutional autoencoder to achieve a high coding efficiency.
Abstract: Image compression has been investigated as a fundamental research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compression. In this paper, we present a lossy image compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to achieve a high coding efficiency. First, we design a novel CAE architecture to replace the conventional transforms and train this CAE using a rate-distortion loss function. Second, to generate a more energy-compact representation, we utilize the principal components analysis (PCA) to rotate the feature maps produced by the CAE, and then apply the quantization and entropy coder to generate the codes. Experimental results demonstrate that our method outperforms traditional image coding algorithms, by achieving a 13.7% BD-rate decrement on the Kodak database images compared to JPEG2000. Besides, our method maintains a moderate complexity similar to JPEG2000.

95 citations


Journal ArticleDOI
TL;DR: Implementation of DICOM allows efficient access to image data as well as associated metadata and facilitates enterprise integration and data exchange for digital pathology by leveraging a wealth of existing infrastructure solutions.

68 citations


Journal ArticleDOI
TL;DR: A novel compression algorithm is presented here, which first spectrally decorrelates the image using Vector Quantization and Principal Component Analysis, and then applies JPEG2000 to the Principal Components exploiting spatial correlations for compression.
Abstract: Compression of hyperspectral imagery increases the efficiency of image storage and transmission. It is especially useful to alleviate congestion in the downlinks of planes and satellites, where these images are usually taken from. A novel compression algorithm is presented here. It first spectrally decorrelates the image using Vector Quantization and Principal Component Analysis (PCA), and then applies JPEG2000 to the Principal Components (PCs) exploiting spatial correlations for compression. We take advantage of the fact that dimensionality reduction preserves more information in the first components, allocating more depth to the first PCs. We optimize the selection of parameters by maximizing the distortion-ratio performance across the test images. An increase of 1 to 3 dB in Signal Noise Ratio (SNR) for the same compression ratio is found over just using PCA + JPEG2000, while also speeding up compression and decompression by more than 10%. A formula is proposed which determines the configuration of the algorithm, obtaining results that range from heavily compressed-low SNR images to low compressed-near lossless ones.

Journal ArticleDOI
TL;DR: Experimental results show that using HEVC can increase the compression performance, compared to JPEG 2000, by over 54%.
Abstract: Efficient storing and retrieval of medical images has direct impact on reducing costs and improving access in cloud-based health care services. JPEG 2000 is currently the commonly used compression format for medical images shared using the DICOM standard. However, new formats such as high efficiency video coding (HEVC) can provide better compression efficiency compared to JPEG 2000. Furthermore, JPEG 2000 is not suitable for efficiently storing image series and 3-D imagery. Using HEVC, a single format can support all forms of medical images. This paper presents the use of HEVC for diagnostically acceptable medical image compression, focusing on compression efficiency compared to JPEG 2000. Diagnostically acceptable lossy compression and complexity of high bit-depth medical image compression are studied. Based on an established medically acceptable compression range for JPEG 2000, this paper establishes acceptable HEVC compression range for medical imaging applications. Experimental results show that using HEVC can increase the compression performance, compared to JPEG 2000, by over 54%. Along with this, a new method for reducing computational complexity of HEVC encoding for medical images is proposed. Results show that HEVC intra encoding complexity can be reduced by over 55% with negligible increase in file size.

Proceedings ArticleDOI
Sanjukta Ghosh, Rohan Shet, Peter Amon1, Andreas Hutter1, Andre Kaup 
15 Apr 2018
TL;DR: This paper analyzes some of the common CNNs for degradations in images caused by Gaussian noise, blur as well as compression using JPEG and JPEG 2000 for the full range of quality factors and proposes a method to improve the performance ofCNNs for image classification in the presence of input images with degradation based on a master-slave architecture.
Abstract: Deep convolutional neural networks (CNNs) have achieved tremendous success in image recognition tasks However, the performance of CNNs degrade in situations where the input image is degraded by compression artifacts, blur or noise In this paper, we analyze some of the common CNNs for degradations in images caused by Gaussian noise, blur as well as compression using JPEG and JPEG 2000 for the full range of quality factors Moreover, we propose a method to improve the performance of CNNs for image classification in the presence of input images with degradations based on a master-slave architecture Our method was found to perform well for individual and combined degradations

Journal ArticleDOI
TL;DR: Compared with the current S-UNIWARD steganography, the message extraction error rates of the proposed algorithm after JPEG compression decrease from about 50 % to nearly 0, and the algorithm not only possesses the comparable JPEG compression resistant ability, but also has a stronger detection resistant performance and a higher operation efficiency.
Abstract: In order to improve the JPEG compression resistant performance of the current steganogrpahy algorithms resisting statistic detection, an adaptive steganography algorithm resisting JPEG compression and detection based on dither modulation is proposed. Utilizing the adaptive dither modulation algorithm based on the quantization tables, the embedding domains resisting JPEG compression for spatial images and JPEG images are determined separately. Then the embedding cost function is constructed by the embedding costs calculation algorithm based on side information. Finally, the RS coding is combined with the STCs to realize the minimum costs messages embedding while improving the correct rates of the extracted messages after JPEG compression. The experimental results demonstrate that the algorithm can be applied to both spatial images and JPEG images. Compared with the current S-UNIWARD steganography, the message extraction error rates of the proposed algorithm after JPEG compression decrease from about 50 % to nearly 0; compared with the current JPEG compression and detection resistant steganography algorithms, the proposed algorithm not only possesses the comparable JPEG compression resistant ability, but also has a stronger detection resistant performance and a higher operation efficiency.

Posted Content
TL;DR: The proposed context-adaptive entropy model exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bit allocation is required, which allows the model to more accurately estimate the distribution of each latent representation with a more generalized form of the approximation models, which accordingly leads to an enhanced compression performance.
Abstract: We propose a context-adaptive entropy model for use in end-to-end optimized image compression. Our model exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bit allocation is required. Based on these contexts, we allow the model to more accurately estimate the distribution of each latent representation with a more generalized form of the approximation models, which accordingly leads to an enhanced compression performance. Based on the experimental results, the proposed method outperforms the traditional image codecs, such as BPG and JPEG2000, as well as other previous artificial-neural-network (ANN) based approaches, in terms of the peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) index.

Journal ArticleDOI
TL;DR: The CoDIFI-NIIRS framework enables a user to broker the maximum compression setting while maintaining a specified NIIRS rating, and model the compression-induced information loss in terms of the National Imagery Interpretability Rating Scale.
Abstract: Image compression is an important component in modern imaging systems as the volume of the raw data collected is increasing. To reduce the volume of data while collecting imagery useful for analysis, choosing the appropriate image compression method is desired. Lossless compression is able to preserve all the information, but it has limited reduction power. On the other hand, lossy compression, which may result in very high compression ratios, suffers from information loss. We model the compression-induced information loss in terms of the National Imagery Interpretability Rating Scale or NIIRS. NIIRS is a user-based quantification of image interpretability widely adopted by the Geographic Information System community. Specifically, we present the Compression Degradation Image Function Index (CoDIFI) framework that predicts the NIIRS degradation (i.e., a decrease of NIIRS level) for a given compression setting. The CoDIFI-NIIRS framework enables a user to broker the maximum compression setting while maintaining a specified NIIRS rating.

Proceedings Article
Haojie Liu1, Tong Chen1, Qiu Shen1, Tao Yue1, Zhan Ma1 
01 Jun 2018
TL;DR: A lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate is presented.
Abstract: We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate. Currently, most of the CNNs based approaches train the network using a L2 loss between the reconstructions and the ground-truths in the pixel domain, which leads to over-smoothing results and visual quality degradation especially at a very low bit rate. Therefore, we improve the subjective quality with the combination of a perception loss and an adversarial loss additionally. To achieve better rate-distortion optimization (RDO), we also introduce an easy-to-hard transfer learning when adding quantization error and rate constraint. Finally, we evaluate our method on public Kodak and the Test Dataset P/M released by the Computer Vision Lab of ETH Zurich, resulting in averaged 7.81% and 19.1% BD-rate reduction over BPG, respectively.

Journal ArticleDOI
TL;DR: An optimized parametrization for JPEG 2000 image compression, designated JP2-WSI, to be used specifically with histopathological WSIs allows very efficient and cost-effective data compression for whole slide images without loss of image information required for Histopathological diagnosis.

Proceedings ArticleDOI
27 Aug 2018
TL;DR: A subjective evaluation of two recent deep-learning-based image compression algorithms, comparing them to JPEG 2000 and to the recent BPG image codec based on HEVC Intra finds that compression approaches based on deep auto-encoders can achieve coding performance higher than JPEG 2000, and sometimes as good as BPG.
Abstract: Image compression standards rely on predictive coding, transform coding, quantization and entropy coding, in order to achieve high compression performance. Very recently, deep generative models have been used to optimize or replace some of these operations, with very promising results. However, so far no systematic and independent study of the coding performance of these algorithms has been carried out. In this paper, for the first time, we conduct a subjective evaluation of two recent deep-learning-based image compression algorithms, comparing them to JPEG 2000 and to the recent BPG image codec based on HEVC Intra. We found that compression approaches based on deep auto-encoders can achieve coding performance higher than JPEG 2000, and sometimes as good as BPG. We also show experimentally that the PSNR metric is to be avoided when evaluating the visual quality of deep-learning-based methods, as their artifacts have different characteristics from those of DCT or wavelet-based codecs. In particular, images compressed at low bitrate appear more natural than JPEG 2000 coded pictures, according to a no-reference naturalness measure. Our study indicates that deep generative models are likely to bring huge innovation into the video coding arena in the coming years.

Posted Content
TL;DR: JPG2000 compression is proposed as an alternative and systematically compared the classification performance of adversarial images compressed using JPEG and JPEG2000 at different target PSNR values and maximum compression levels show that JPEG2000 is more effective in reducing adversarial noise.
Abstract: Adversarial examples are known to have a negative effect on the performance of classifiers which have otherwise good performance on undisturbed images. These examples are generated by adding non-random noise to the testing samples in order to make classifier misclassify the given data. Adversarial attacks use these intentionally generated examples and they pose a security risk to the machine learning based systems. To be immune to such attacks, it is desirable to have a pre-processing mechanism which removes these effects causing misclassification while keeping the content of the image. JPEG and JPEG2000 are well-known image compression techniques which suppress the high-frequency content taking the human visual system into account. JPEG has been also shown to be an effective method for reducing adversarial noise. In this paper, we propose applying JPEG2000 compression as an alternative and systematically compare the classification performance of adversarial images compressed using JPEG and JPEG2000 at different target PSNR values and maximum compression levels. Our experiments show that JPEG2000 is more effective in reducing adversarial noise as it allows higher compression rates with less distortion and it does not introduce blocking artifacts.

Journal ArticleDOI
TL;DR: This paper proposes a technique for image watermarking during JPEG compression to address the optimal trade-off between major performance parameters including embedding and compression rates, robustness and embedding alterations against different known signal processing attacks.
Abstract: This paper presents a computationally efficient joint imperceptible image watermarking and joint photographic experts group (JPEG) compression scheme In recent times, the transmission and storage of digital documents/information over the unsecured channel are enormous concerns and nearly all of the digital documents are compressed before they are stored or transmitted to save the bandwidth requirements There are many similar computational operations performed during watermarking and compression which lead to computational redundancy and time delay This demands development of joint watermarking and compression scheme for various multimedia contents In this paper, we propose a technique for image watermarking during JPEG compression to address the optimal trade-off between major performance parameters including embedding and compression rates, robustness and embedding alterations against different known signal processing attacks The performance of the proposed technique is extensively evaluated in the form of peak signal to noise ratio (PSNR), correlation, compression ratio and execution time for different discrete cosine transform (DCT) blocks and watermark sizes Embedding is done on DCT coefficients using additive watermarking

Journal ArticleDOI
Nan Jiang, Xiaowei Lu1, Hao Hu1, Yijie Dang1, Yongquan Cai1 
TL;DR: This paper uses GQIR (the generalized quantum image representation) to represent an image, and tries to decrease the operations used in preparation, which is also known as quantum image compression.
Abstract: Quantum image processing has been a hot topic. The first step of it is to store an image into qubits, which is called quantum image preparation. Different quantum image representations may have different preparation methods. In this paper, we use GQIR (the generalized quantum image representation) to represent an image, and try to decrease the operations used in preparation, which is also known as quantum image compression. Our compression scheme is based on JPEG (named from its inventor: the Joint Photographic Experts Group) — the most widely used method for still image compression in classical computers. We input the quantized JPEG coefficients into qubits and then convert them into pixel values. Theoretical analysis and experimental results show that the compression ratio of our scheme is obviously higher than that of the previous compression method.

Proceedings ArticleDOI
06 Oct 2018
TL;DR: This paper presents an iterative nonuniform quantization scheme for deep image compression that alternatively optimize the quantizer and encoder-decoder, and demonstrates the superior PSNR index of the proposed method to existing deep compressors and JPEG2000.
Abstract: Image compression, which aims to represent an image with less storage space, is a classical problem in image processing. Recently, by training an encoder-quantizer-decoder network, deep convolutional neural networks (CNNs) have achieved promising results in image compression. As a nondifferentiable part of the compression system, quantizer is hard to be updated during the network training. Most of existing deep image compression methods adopt a uniform rounding function as the quantizer, which however restricts the capability and flexibility of CNNs in compressing complex image structures. In this paper, we present an iterative nonuniform quantization scheme for deep image compression. More specifically, we alternatively optimize the quantizer and encoder-decoder. When the encoder-decoder is fixed, a non-uniform quantizer is optimized based on the distribution of representation features. The encoder-decoder network is then updated by fixing the quantizer. Extensive experiments demonstrate the superior PSNR index of the proposed method to existing deep compressors and JPEG2000.

Journal ArticleDOI
TL;DR: Experimental results show that the proposed method can significantly improve the image compression performance compared with JPEG, JPEG2000, and the state-of-the-art dictionary learning-based methods.
Abstract: Image set compression has recently emerged as an active research topic due to the rapidly increasing demand in cloud storage. In this paper, we propose a novel framework for image set compression based on the rate-distortion optimized sparse coding. Specifically, given a set of similar images, one representative image is first identified according to the similarity among these images, and a dictionary can be learned subsequently in wavelet domain from the training samples collected from the representative image. In order to improve coding efficiency, the dictionary atoms are reordered according to their use frequencies when representing the representative image. As such, the remaining images can be efficiently compressed with sparse coding based on the reordered dictionary that is highly adaptive to the content of the image set. To further improve the efficiency of sparse coding, the number of dictionary atoms for image patches is further optimized in a rate-distortion sense. Experimental results show that the proposed method can significantly improve the image compression performance compared with JPEG, JPEG2000, and the state-of-the-art dictionary learning-based methods.

Journal ArticleDOI
TL;DR: A hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images is proposed, which exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance.
Abstract: To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively.

Proceedings ArticleDOI
18 Jun 2018
TL;DR: In this article, the authors propose a deformation-insensitive error measure that can be easily incorporated into any existing lossy compression scheme, such as JPEG, JPEG 2000, WebP, and BPG.
Abstract: Lossy compression algorithms aim to compactly encode images in a way which enables to restore them with minimal error. We show that a key limitation of existing algorithms is that they rely on error measures that are extremely sensitive to geometric deformations (e.g. SSD, SSIM). These force the encoder to invest many bits in describing the exact geometry of every fine detail in the image, which is obviously wasteful, because the human visual system is indifferent to small local translations. Motivated by this observation, we propose a deformation-insensitive error measure that can be easily incorporated into any existing compression scheme. As we show, optimal compression under our criterion involves slightly deforming the input image such that it becomes more "compressible". Surprisingly, while these small deformations are barely noticeable, they enable the CODEC to preserve details that are otherwise completely lost. Our technique uses the CODEC as a "black box", thus allowing simple integration with arbitrary compression methods. Extensive experiments, including user studies, confirm that our approach significantly improves the visual quality of many CODECs. These include JPEG, JPEG 2000, WebP, BPG, and a recent deep-net method.

Proceedings ArticleDOI
24 May 2018
TL;DR: It becomes evident that future representation standards should consider the representation of digital holograms on the object plane instead of the hologram plane, and the HEVC intra main coding profile is a very efficient model and outperforms the other standards.
Abstract: Digital holography is a growing field that owes its success to the provided three-dimensional imaging representation. This is achieved by encoding the wave field transmitted or scattered by an object in the form of an interference pattern with a reference beam. While in conventional imaging systems it is usually impossible to recover the correct focused image from a defocused one, with digital holography the image can be numerically retrieved at any distance from the hologram. Digital holography also allows the reconstruction of multiple objects at different depths. The complex object field at the hologram plane can be separated on real and imaginary, or amplitude and phase components for further compression. It could be inferred that more inter-component redundancies exist in real and imaginary information than in the amplitude and phase information. Also, several compression schemes, like lossless compression, lossy compression, based on subsampling, quantization, and transformation, mainly using wavelets were considered. The benchmark of the main available image coding standard solutions like JPEG, JPEG 2000, and the intra coding modes available on MPEG-2, H264/AVC and HEVC video codecs were also analyzed for digital holographic data compression on the hologram plane. In the current work, the benchmark of the main available image coding standard solutions JPEG, JPEG-XT, JPEG 2000 and the intra mode of HEVC, are performed for digital holographic data represented on the object plane, instead of the hologram plane. This study considers Real-Imaginary and Amplitude-Phase representations. As expected Real, Imaginary and Amplitude information present very similar compression performance and are coded very efficiently with the different standards. However, the phase information requires much higher bitrates (3/4 bpp more) to reach similar quality levels. Moreover, the Amplitude information results in slightly larger bitrates for the same quality level than real or imaginary information. Comparing the different standards, the HEVC intra main coding profile is a very efficient model and outperforms the other standards. Furthermore, JPEG 2000 results in very similar compression performance. A comparison with studies where coding was performed on the hologram plane will reveal the advantages of coding on the object plane. Hence, becomes evident that future representation standards should consider the representation of digital holograms on the object plane instead of the hologram plane.

Journal ArticleDOI
TL;DR: A research study of lossless light field image compression, using Minimum-Rate Predictors (MRP) and mainstream image and video encoders, focused on three light field representation formats: lenslet images, stack of sub-aperture images and epipolar images.

Journal ArticleDOI
TL;DR: A block-permutation-based encryption (BPBE) scheme for the encryption-then-compression (ETC) system that enhances the color scrambling and can maintain the JPEG-LS compression efficiency compared to the conventional scheme is proposed.
Abstract: This paper proposes a block-permutation-based encryption (BPBE) scheme for the encryption-then-compression (ETC) system that enhances the color scrambling. A BPBE image can be obtained through four processes, positional scrambling, block rotation/flip, negative-positive transformation, and color component shuffling, after dividing the original image into multiple blocks. The proposed scheme scrambles the R, G, and B components independently in positional scrambling, block rotation/flip, and negative-positive transformation, by assigning different keys to each color component. The conventional scheme considers the compression efficiency using JPEG and JPEG 2000, which need a color conversion before the compression process by default. Therefore, the conventional scheme scrambles the color components identically in each process. In contrast, the proposed scheme takes into account the RGB-based compression, such as JPEG-LS, and thus can increase the extent of the scrambling. The resilience against jigsaw puzzle solver (JPS) can consequently be increased owing to the wider color distribution of the BPBE image. Additionally, the key space for resilience against brute-force attacks has also been expanded exponentially. Furthermore, the proposed scheme can maintain the JPEG-LS compression efficiency compared to the conventional scheme. We confirm the effectiveness of the proposed scheme by experiments and analyses.

Proceedings ArticleDOI
01 Nov 2018
TL;DR: This research is to compare and select the best compression algorithm in the literature to achieve 8:1 or more compression ratio with perceptually lossless compression for Mastcam images.
Abstract: The communication channel between Mars and Earth is bandwidth limited. In such bandwidth scarce applications, lossless compression of Mastcam images may be unnecessary, as only two to three times of compression can be achieved. Currently, NASA is using JPEG codec, a technology invented in the 90’s, to compress the Mastcam images. An alternative way to save bandwidth is to adopt perceptually lossless compression, which can attain eight times or more compression without loss of important information. In this research, our main objective is to compare and select the best compression algorithm in the literature to achieve 8:1 or more compression ratio with perceptually lossless compression for Mastcam images. We have clearly achieved the above objectives using real Mastcam images.