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Logarithmical hopping encoding: a low computational complexity algorithm for image compression

IET image PROCESSING 2015年第8期9卷 643-651页

作者： Garcia Aranda, Jose Javier Gonzalez Casquete, Marina Cao Cueto, Mario Navarro Salmeron, Joaquin Gonzalez Vidal, Francisco Alcatel Lucent Video Architecture Dept Madrid Spain UPM Dept Ingn Sistemas Telemat DIT Madrid Spain

LHE (logarithmical hopping encoding) is a computationally efficient image compression algorithm that exploits the Weber-Fechner law to encode the error between colour component predictions and the actual value of such components. More concretely, for each pixel, luminance and chrominance predictions are calculated as a function of the surrounding pixels and then the error between the predictions and the actual values are logarithmically quantised. The main advantage of LHE is that although it is capable of achieving a low-bit rate encoding with high quality results in terms of peak signal-to-noise ratio (PSNR) and image quality metrics with full-reference (FSIM) and non-reference (blind/referenceless image spatial quality evaluator), its time complexity is O(n) and its memory complexity is O(1). Furthermore, an enhanced version of the algorithm is proposed, where the output codes provided by the logarithmical quantiser are used in a pre-processing stage to estimate the perceptual relevance of the image blocks. This allows the algorithm to downsample the blocks with low perceptual relevance, thus improving the compression rate. The performance of LHE is especially remarkable when the bit per pixel rate is low, showing much better quality, in terms of PSNR and FSIM, than JPEG and slightly lower quality than JPEG-2000 but being more computationally efficient.

关键词： image coding data compression computational complexity image colour analysis LHE logarithmical hopping encoding image compression algorithm Weber-Fechner law colour component predictions luminance predictions chrominance predictions low-bit rate encoding peak signal-to-noise ratio PSNR image quality metrics full-reference FSIM blind image spatial quality evaluator referenceless image spatial quality evaluator output codes logarithmical quantiser preprocessing stage perceptual relevance image blocks compression rate

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Non-reference image quality assessment and natural scene statistics to counter biometric sensor spoofing

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IET BIOMETRICS 2018年第4期7卷 314-324页

作者： Soellinger, Dominik Trung, Pauline Uhl, Andreas Univ Salzburg Dept Comp Sci Jakob Haringer Str 2 A-5020 Salzburg Austria

Non-reference image quality measures (IQM) as well as their associated natural scene statistics (NSS) are used to distinguish real biometric data from fake data as used in presentation/sensor spoofing attacks. An experimental study shows that a support vector machine directly trained on NSS as used in blind/referenceless image spatial quality evaluator provides highly accurate classification of real versus fake iris, fingerprint, face, and fingervein data in generic manner. This contrasts to using the IQM directly, the accuracy of which turns out to be rather data set and parameter choice-dependent. While providing very low average classification error rate values for complete training data, generalisation to unseen attack types is difficult in open-set scenarios and obtained accuracy varies in almost unpredictable manner. This implies that for each given sensor/attack set-up, the ability of the introduced methods to detect unseen attacks needs to be assessed separately.

关键词： feature extraction learning (artificial intelligence) statistical analysis iris recognition pattern classification image processing natural scenes support vector machines biometrics (access control) fingerprint identification fake data presentation sensor spoofing attacks experimental study support vector machine NSS blind referenceless image spatial quality evaluator highly accurate classification versus fake iris fingervein data generic manner IQM low average classification error rate values complete training data unseen attack types open-set scenarios given sensor attack unseen attacks nonreference image quality assessment biometric sensor spoofing nonreference image quality measures associated natural scene statistics biometric data

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Non-reference image quality assessment and natural scene statistics to counter biometric sensor spoofing

Non-reference image quality assessment and natural scene sta...

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5th International Workshop on Biometrics and Forensics (IWBF)

作者： Soellinger, Dominik Trung, Pauline Uhl, Andreas Univ Salzburg Dept Comp Sci Jakob Haringer Str 2 A-5020 Salzburg Austria

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