Prediction of Compression Ratio for DCT-Based Coders With Application to Remote Sensing Images

Zemliachenko, Alexander; Kozhemiakin, Ruslan; Abramov, Sergey; Vozel, Benôıt; Chehdi, Kacem; Егиазарян, Карен

doi:10.1109/jstars.2017.2781906

Cited by 15 publications

(11 citation statements)

References 38 publications

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“…where λ 1 and λ 2 are the regularization parameters. To efficiently calculateŴ andÛ, here, we use Equations (13) and (14) to replace Equations (11) and (12) by adding a regularization term as:…”

Section: Proposed Multispectral Tensor With Cnnsmentioning

confidence: 99%

“…where β 1 and β 2 are the regularization parameters. The detailed derivation procedure from Equations (11) and (12) to Equations (13) and (14) can be seen in Appendix A. The two-step learning algorithm of the multispectral transform can be summarized, as given below, which is used to obtain the best small-scale spectral tensor and CNN parameters.…”

Section: Proposed Multispectral Tensor With Cnnsmentioning

confidence: 99%

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Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

Liu

2019

Remote Sensing

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A multispectral image is a three-order tensor since it is a three-dimensional matrix, i.e.one spectral dimension and two spatial position dimensions. Multispectral image compression canbe achieved by means of the advantages of tensor decomposition (TD), such as NonnegativeTucker Decomposition (NTD). Unfortunately, the TD suffers from high calculation complexity andcannot be used in the on-board low-complexity case (e.g., multispectral cameras) that the hardwareresources and power are limited. Here, we propose a low-complexity compression approach formultispectral images based on convolution neural networks (CNNs) with NTD. We construct anew spectral transform using CNNs, where the CNNs are able to transform the three-dimensionspectral tensor from large-scale to a small-scale version. The NTD resources only allocate thesmall-scale three-dimension tensor to improve calculation efficiency. We obtain the optimizedsmall-scale spectral tensor by the minimization of original and reconstructed three-dimensionspectral tensor in self-learning CNNs. Then, the NTD is applied to the optimized three-dimensionspectral tensor in the DCT domain to obtain the high compression performance. We experimentallyconfirmed the proposed method on multispectral images. Compared to the case that the newspectral tensor transform with CNNs is not applied to the original three-dimension spectral tensorat the same compression bit-rates, the reconstructed image quality could be improved. Comparedwith the full NTD-based method, the computation efficiency was obviously improved with only asmall sacrifices of PSNR without affecting the quality of images.

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Section: Proposed Multispectral Tensor With Cnnsmentioning

confidence: 99%

Section: Proposed Multispectral Tensor With Cnnsmentioning

confidence: 99%

Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

Liu

2019

Remote Sensing

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“…Subsequently, Zemliachenko et al developed a compression ratio prediction algorithm for a discrete cosine transform (DCT) encoder, utilizing remote sensing images. Building upon the discrete wavelet transform (DWT), Dheepa et al introduced a directional lifting wavelet transform (DLWT) [24,25]. By constructing a transformation matrix, implementing internal quantization, and employing a general function for the encoding and decoding processes, they aimed to enhance the coding efficiency and clustering capabilities.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

Deng,

Huang

2024

Remote Sensing

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Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing. However, there is a limited body of literature specifically addressing the compression of SAR learning images. To address the escalating volume of SAR image data for storage and transmission, which necessitates more effective compression algorithms, this paper proposes a novel framework for compressing SAR images. Experimental validation is performed using a representative low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. Initially, we introduce a novel two-stage transformation-based approach aimed at suppressing the low-frequency components of the input data, thereby achieving a high information entropy and minimizing quantization losses. Subsequently, a quality map guidance image compression algorithm is introduced, involving the fusion of the input SAR images with a target-aware map. This fusion involves convolutional transformations to generate a compact latent representation, effectively exploring redundancies between focused and non-focused areas. To assess the algorithm’s performance, experiments are carried out on both the low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. The results indicate that the low-frequency suppression algorithm significantly outperforms traditional processing algorithms by 3–8 dB when quantifying the input data, effectively preserving image features and improving image performance metrics. Furthermore, the quality map guidance image compression algorithm demonstrates a superior performance compared to the baseline model.

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“…There are several reasons behind this. First, compression providing a fixed CR often used in practice [21,22] leads to compressed images whose quality can vary in wide limits [23]. For a given CR and simpler structure images, introduced distortions are smaller and compressed image quality is higher, whilst for images, having a more complex structure (containing many small-sized details and textures), losses are larger and, thus, image quality can be inappropriate since some important information can be inevitably lost, which is undesired.…”

Section: Introductionmentioning

confidence: 99%

“…For a given CR and simpler structure images, introduced distortions are smaller and compressed image quality is higher, whilst for images, having a more complex structure (containing many small-sized details and textures), losses are larger and, thus, image quality can be inappropriate since some important information can be inevitably lost, which is undesired. Certainly, some improvements can be reached due to the employment of a better coder, adaptation to image content [24], use of inter-channel correlation by three-dimensional (3D) compression [12,13,21,22], or some other means. However, the positive effect can be limited, and/or there can be some restrictions, e.g., the necessity to apply image compression standard [12,19,21].…”

Section: Introductionmentioning

confidence: 99%

Lossy Compression of Multichannel Remote Sensing Images with Quality Control

et al. 2020

Self Cite

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Lossy compression is widely used to decrease the size of multichannel remote sensing data. Alongside this positive effect, lossy compression may lead to a negative outcome as making worse image classification. Thus, if possible, lossy compression should be carried out carefully, controlling the quality of compressed images. In this paper, a dependence between classification accuracy of maximum likelihood and neural network classifiers applied to three-channel test and real-life images and quality of compressed images characterized by standard and visual quality metrics is studied. The following is demonstrated. First, a classification accuracy starts to decrease faster when image quality due to compression ratio increasing reaches a distortion visibility threshold. Second, the classes with a wider distribution of features start to “take pixels” from classes with narrower distributions of features. Third, a classification accuracy might depend essentially on the training methodology, i.e., whether features are determined from original data or compressed images. Finally, the drawbacks of pixel-wise classification are shown and some recommendations on how to improve classification accuracy are given.

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Prediction of Compression Ratio for DCT-Based Coders With Application to Remote Sensing Images

Cited by 15 publications

References 38 publications

Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

Lossy Compression of Multichannel Remote Sensing Images with Quality Control

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