Low Complexity Pixel Domain Perceptual Image Compression via Adaptive Down-Sampling

2016 ◽  
Author(s):  
Zhe Wang ◽  
Sven Simon
Keyword(s):  
Image Compression ◽  
Low Complexity ◽  
Perceptual Image Compression

Efficient Domain Search for Fractal Image Compression Using Feature Extraction Technique

2012 ◽  
Vol 488-489 ◽  
pp. 1587-1591
Author(s):  
Amol G. Baviskar ◽  
S. S. Pawale
Keyword(s):  
Feature Extraction ◽  
Image Compression ◽  
Low Complexity ◽  
Contractive Mapping ◽  
Fractal Image Compression ◽  
Compression Technique ◽  
Image Encoding ◽  
Fractal Image ◽  
Coding Method ◽  
The Given

Fractal image compression is a lossy compression technique developed in the early 1990s. It makes use of the local self-similarity property existing in an image and finds a contractive mapping affine transformation (fractal transform) T, such that the fixed point of T is close to the given image in a suitable metric. It has generated much interest due to its promise of high compression ratios with good decompression quality. Image encoding based on fractal block-coding method relies on assumption that image redundancy can be efficiently exploited through block-self transformability. It has shown promise in producing high fidelity, resolution independent images. The low complexity of decoding process also suggested use in real time applications. The high encoding time, in combination with patents on technology have unfortunately discouraged results. In this paper, we have proposed efficient domain search technique using feature extraction for the encoding of fractal image which reduces encoding-decoding time and proposed technique improves quality of compressed image.

Low-Complexity Approaches for Lossless and Near-Lossless Hyperspectral Image Compression

2011 ◽  
pp. 47-65
Author(s):  
Andrea Abrardo ◽  
Mauro Barni ◽  
Andrea Bertoli ◽  
Raoul Grimoldi ◽  
Enrico Magli ◽  
...  
Keyword(s):  
Image Compression ◽  
Hyperspectral Image ◽  
Low Complexity ◽  
Hyperspectral Image Compression

Implementation of LOCO-I Lossless Image Compression Algorithm for Deep Space Applications

2014 ◽  
Vol 3 (3) ◽  
pp. 98
Author(s):  
P. Praveena
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Low Complexity ◽  
Vital Role ◽  
Space Science ◽  
Compression Algorithm ◽  
Deep Space ◽  
Space Applications ◽  
Lossless Image Compression ◽  
Image Compression Algorithm

<p>Present emerging trend in space science applications is to explore and utilize the deep space. Image coding in deep space communications play vital role in deep space missions. Lossless image compression has been recommended for space science exploration missions to retain the quality of image. On-board memory and bandwidth requirement is reduced by image compression. Programmable logic like field programmable gate array (FPGA) offers an attractive solution for performance and flexibility required by real time image compression algorithms. The powerful feature of FPGA is parallel processing which allows the data to process quicker than microprocessor implementation. This paper elaborates on implementing low complexity lossless image compression algorithm coder on FPGA with minimum utilization of onboard resources for deep space applications.</p>

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

2019 ◽  
Vol 11 (7) ◽  
pp. 759 ◽  
Author(s):  
Jin Li ◽  
Zilong Liu
Keyword(s):  
Neural Networks ◽  
Image Compression ◽  
Low Complexity ◽  
Multispectral Image ◽  
Small Scale ◽  
Three Dimension ◽  
Convolution Neural Networks ◽  
Computation Efficiency ◽  
Spectral Transform ◽  
Multispectral Image Compression

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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