MSE and PSNR prediction for ADCT coder applied to lossy image compression

International audienceLossy compression of images is used for numerous applications nowadays. Typical requirements to it are to provide a higher compression ratio (CR) for a desired quality and to perform this quickly enough. Advanced discrete cosine transform (ADCT) based coder potentially provides a good compromise between CR and image quality but consumes essential resources for reaching a desired quality of compressed data. To get around this shortcoming, we develop a fast and rather accurate approach to prediction of mean square error (MSE) or peak signal-to-noise ratio (PSNR). This approach performs sufficiently faster than compression and allows saving time and resources. It is shown that if an image to be compressed is corrupted by noise, prediction correction is possible and desirable. © 2018 IEEE

MSE prediction in DCT-based lossy compression of noise-free and noisy remote sensing

International audienceAmount of remote sensing data rapidly increases. To transfer, store, and disseminate images, one has to apply compression. Lossless compression often does not satisfy requirements. In turn, lossy compression introduces distortions where their level should be controlled not to lose value of remote sensing data. In this paper, we propose a way to accurately predict mean square error of introduced distortions for DCT-based coder. Peculiarity of our approach is that prediction is considerably faster than compression. This allows estimating the introduced distortion level for a given quantization step and quick adjusting the quantization step. The proposed approach is applicable to both practically noise-free and noisy remote sensing images. © 2018 IEEE

Application of filtering efficiency prediction to hyperspectral data pre-processing

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Automatic Approaches to On-Land/On-Board Filtering and Lossy Compression of AVIRIS Images

Hyperspectral imaging is widely used in different applications [1]. However, the obtained data occupy rather large volume and their compression is important for data transmission via communication lines, transferring to customers and/or archiving [2]. Lossless compression is often unable to produce a desirable compression ratio (CR) [1, 2], thus, lossy compression techniques are widely applied [3]. For lossy compression, there are two conflicting requirements – to provide larger CR an