Abstract
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 can be achieved by means of the advantages of tensor decomposition (TD), such as Nonnegative Tucker Decomposition (NTD). Unfortunately, the TD suffers from high calculation complexity and cannot be used in the on-board low-complexity case (e.g., multispectral cameras) that the hardware resources and power are limited. Here, we propose a low-complexity compression approach for multispectral images based on convolution neural networks (CNNs) with NTD. We construct a new spectral transform using CNNs, where the CNNs are able to transform the three-dimension spectral tensor from large-scale to a small-scale version. The NTD resources only allocate the small-scale three-dimension tensor to improve calculation efficiency. We obtain the optimized small-scale spectral tensor by the minimization of original and reconstructed three-dimension spectral tensor in self-learning CNNs. Then, the NTD is applied to the optimized three-dimension spectral tensor in the DCT domain to obtain the high compression performance. We experimentally confirmed the proposed method on multispectral images. Compared to the case that the new spectral tensor transform with CNNs is not applied to the original three-dimension spectral tensor at the same compression bit-rates, the reconstructed image quality could be improved. Compared with the full NTD-based method, the computation efficiency was obviously improved with only a small sacrifices of PSNR without affecting the quality of images.
Highlights
Multispectral images are acquired by a multispectral camera that is able to collect the reflected, emitted, or backscattered energy from an object or scene in multiple bands of the electromagnetic spectrum [1,2,3,4,5]
RtehmisotemSeentsh. o20d19g, a11in, 7s59higher PSNR compared with Principal Components Analysis (PCA) + JPEG3 2o0f 2000 and 2D Set Partitioned Embedded Block Coder (SPECK)
To evaluate the compression performance of the proposed algorithm with the spectral transform using convolution neural networks (CNNs), we used MATLAB to perform the experiments on a personal computer (PC)
Summary
Multispectral images are acquired by a multispectral camera that is able to collect the reflected, emitted, or backscattered energy from an object or scene in multiple bands of the electromagnetic spectrum [1,2,3,4,5]. The main advantage of the prediction-based approaches is their low complexity The main disadvantages of the prediction-based approaches are low compression performance and their weak fault-tolerance ability. Existing encoders (e.g., bit-plane coding or entropy encoding methods) process the transformed coefficients. This method has many advantages, such as high compression performance, high fault-tolerance performance, and the possibility of fast calculations. From the 3D data inherent structure aspect, the transform-based approaches may crush the inherent structure of a multispectral image and mask redundancy information, which would result in the high-order dependencies that still existed in images because the compression process regards the multispectral image as a matrix
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