Encryption for high efficiency video coding with video adaptation capabilities

Video encryption techniques enable applications like digital rights management and video scrambling. Applying encryption on the entire video stream can be computationally costly and prevents advanced video modifications by an untrusted middlebox in the network, like splicing, quality monitoring, watermarking, and transcoding. Therefore, encryption techniques are proposed which influence a small amount of the video stream while keeping the video compliant with its compression standard, High Efficiency Video Coding. Encryption while guaranteeing standard compliance can cause degraded compression efficiency, so depending on their bitrate impact, a selection of encrypted syntax elements should be made. Each element also impacts the quality for untrusted decoders differently, so this aspect should also be considered. In this paper, multiple techniques for partial video encryption are investigated, most of them having a low impact on rate-distortion performance and having a broad range in scrambling performance(1)

Description of flower colors for image based plant species classification

Apart from shape, color is the most visually prominent and perceivable feature of a flower. To use color as a feature for fine-grained plant species classification based on flower images, its descriptor has to be discriminative, compact, and robust against photometric variations. Therefore, we studied state-of-the-art color description methods and evaluated their discriminative power in an image classification pipeline. Experiments have been performed on three flower image datasets possessing large photometric and geometric varieties. We found that implicit photometric invariance by pooling 11 basic colors from patches around local features allows for robust color description outperforming explicitly photometric invariant descriptors in most cases

Flora Incognita – Halbautomatische Bestimmung der Pflanzenarten Thüringens mit dem Smartphone

Species knowledge is essential for protecting biodiversity. People are more willing to protect plants and animals that they personally experienced before. The identification of plants by conventional keys is very complex, time consuming, and due to the use of specific terms frustrating for non-experts. This creates a hard to overcome hurdle for novices interested in acquiring species knowledge. Modern communication techniques are a continuous companion in today’s life and provide an opportunity to simplify conventional identification methods. The goal of our “Flora Incognita” project is developing a method for semi-automatic plant identification via mobile devices. The process will lead a user through an interactive series of identification steps. Part of these steps will utilise image recognition techniques to identify plant traits. An accompanying web-based platform will allow ambitious interested users to contribute in our project