6,486 research outputs found

    Statistical lossless compression of space imagery and general data in a reconfigurable architecture

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    A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images

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    Predictive coding is attractive for compression onboard of spacecrafts thanks to its low computational complexity, modest memory requirements and the ability to accurately control quality on a pixel-by-pixel basis. Traditionally, predictive compression focused on the lossless and near-lossless modes of operation where the maximum error can be bounded but the rate of the compressed image is variable. Rate control is considered a challenging problem for predictive encoders due to the dependencies between quantization and prediction in the feedback loop, and the lack of a signal representation that packs the signal's energy into few coefficients. In this paper, we show that it is possible to design a rate control scheme intended for onboard implementation. In particular, we propose a general framework to select quantizers in each spatial and spectral region of an image so as to achieve the desired target rate while minimizing distortion. The rate control algorithm allows to achieve lossy, near-lossless compression, and any in-between type of compression, e.g., lossy compression with a near-lossless constraint. While this framework is independent of the specific predictor used, in order to show its performance, in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless compression standard, obtaining an extension that allows to perform lossless, near-lossless and lossy compression in a single package. We show that the rate controller has excellent performance in terms of accuracy in the output rate, rate-distortion characteristics and is extremely competitive with respect to state-of-the-art transform coding

    Backward adaptive pixel-based fast predictive motion estimation

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    Memory-efficient lossless video compression using temporal extended JPEG-LS and on-line compression

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    Use of temporal predictors in lossless video coders play a significant role in terms of compression gain, but comes with a cost of significant memory requirement since this approach requires to save at least one frame in buffer for residue calculation. An improvement to standard JPEG-LS based lossless video coding algorithm is proposed in this work which requires very small amount of memory comparing to the regular approach keeping the computational complexity low. To obtain a higher compression, a combination of spatial and temporal predictor model has been used where appropriate mode is selected adaptively on a pixel based analysis. Using only one reference frame, the context based temporal coder performs its calculation regarding mode selection and prediction error calculation with already reconstructed pixels. This method eliminates the overhead of transmitting the coding mode in the decoder side. The need for storage space to save the only reference frame is further reduced by introducing on-line lossy compression on that frame. Relevant pixels from the stored reference frame are obtained by partial on-the-fly decompression. The combination of temporally extended context based prediction and on-line compression achieves a significant gain in compression ratio comparing to standard frame-by-frame JPEG-LS video coding keeping the memory requirement low, making it usable as a lightweight lossless video coder for embedded systems
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