Second, we develop a universal quantization for the obtained CS measurements from CS acquisition without knowing any a priori information about the captured image. First, we design an adaptive compressive sensing acquisition method for images according to the sampling rate, which could achieve better CS reconstruction quality for the acquired image. In this paper, we propose an efficient lossy compression solution for CS acquisition of images by considering the distinctive features of the CSI. Since the characteristics of Compressive Sensing (CS acquisition are very different from traditional image acquisition, the general image compression solution may not work well. As an example, we describe its importance for e-commerce applications with watermarking security.Įfficient Lossy Compression for Compressive Sensing Acquisition of Images in Compressive Sensing Imaging Systemsĭirectory of Open Access Journals (Sweden)įull Text Available Compressive Sensing Imaging (CSI is a new framework for image acquisition, which enables the simultaneous acquisition and compression of a scene. Our model can be used for audio watermarking evaluation of numerous application fields. This method is less complex, as no real psycho acoustic model has to be applied. We discuss how such a model can be implemented based on the results of our tests.
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There are two strategies for adding evaluation of robustness against lossy compression to StirMark Benchmark: (a) use of existing free compression algorithms (b) implementation of a generic lossy compression simulation. Furthermore we compare results of different watermarking algorithms and show that lossy compression is still a challenge for most of them. Our focus is on changes regarding the basic characteristics of the audio data like spectrum or average power and on removal of embedded watermarks. We discuss the effect of different lossy compression algorithms like MPEG-2 audio Layer 3, Ogg or VQF on a selection of audio test data. To enable application based evaluation, in our paper we address attacks against audio watermarks based on lossy audio compression algorithms to be included in the test environment. Additional attacks are added to it continuously. StirMark Benchmark is a well-known evaluation tool for watermarking robustness. Steinebach, Martin Lang, Andreas Dittmann, Jana
StirMark Benchmark: audio watermarking attacks based on lossy compression
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