In literature about selective encryption of image and video content, image quality indices are usually used to gauge the degree of encryption. These methods have frequently been shown not to work well for the evaluation of encryption, mainly due to them being trained on predominantly high quality contents. The problem for creating a proper recognition index or visual encryption strength index is the lack of data to train on. In this paper we present the first database of encrypted video content, ranging from high quality to completely unrecognizable, together with human observer scores for quality and recognizability. We also provide a basic evaluation of visual quality indices on this database, directly and in different combination by fusion, to showcase that currently image and video quality indices are ill fit for the purpose of estimating video encryption strength/recognizably. This inability of quality indices to perform also showcases that this database fills the required blind spot of currently available data.

The assessment of very low quality visual data is known to be difficult. In particular, the ability of humans to recognize encrypted visual data is currently impossible to determine computationally. The human vision research community has widely studied some particular topics, such as image quality assessment or the determination of a visibility threshold, while others are still barely researched, specifically visual content recognition. To this day, there does not exist a reliable recognition index that can be employed for such tasks. In order to enable the study of human image content recognition, and in an attempt to propose a corresponding recognizability index, we build a dataset of selectively encrypted images together with subjective ground-truth about their human intelligibility. The methods of acquisition, setup, protocol, outlier detection, are described and we suggest how to calculate a recognition score as well as a recognition threshold. The performance of traditional visual quality indices to predict human visual content recognition is assessed on these data and found to be inapt to estimate recognition of visual content. Contrasting, structure based recognition indices as proposed for this task are shown to represent a promising starting point for further research. To facilitate the creation of a recognition index and to foster further research into human visual content recognition and its relation to the human visual system we will make the database publicly available.

Summary form only given. The authors introduce hybrid algorithms which consist of a fractal predictor in the spatial domain with subsequent coding of the residual image (error-image between the fractal prediction and the image to compress). For coding the residual either wavelet (based on the SPIHT coder) or DCT based coding (as used in interframe compression, e.g. for B or P frames in H.261, MPEG-1,2) is employed. Additionally they contribute to the discussion about the performance of wavelet and DCT based algorithms for the compression of motion compensated error frames in interframe video coding algorithms since the residual images considered in the proposed hybrid algorithms exhibit similar (or even identical) statistical properties as motion compensated error frames.