Audiovisual communications is at the core of multimedia systems that allow users to interact across distances. It is common understanding that both audio and video are required for high-quality interaction. While audiovisual information provides a user with a satisfactory impression of being present in a remote environment, physical interaction and manipulation is not supported. True immersion into a distant environment and efficient distributed collaboration require the ability to physically interact with remote objects and to literally get in touch with other people. Touching and manipulating objects remotely becomes possible if we augment traditional audiovisual communications by the haptic modality. Haptic communications is a relatively young field of research that has the potential to substantially improve human-human and human-machine interaction. In this paper, we discuss the state-of-the-art in haptic communications both from psychophysical and technical points of view. From a human perception point of view, we mainly focus on the multimodal integration of video and haptics and the improved performance that can be achieved when combining them. We also discuss how the human adapts to discrepancies and synchronization errors between different modalities, a research area which is typically referred to as perceptual learning. From a technical perspective, we address perceptual coding of haptic information and the transmission of haptic data streams over resource-constrained and potentially lossy networks in the presence of unpredictable and time-varying communication delays. In this context, we also discuss the need for objective quality metrics for haptic communication. Throughout the paper, we stress the fact that haptic communications is not meant as a replacement of traditional audiovisual communications but rather as an additional dimension for telepresence that will allow us to advance in our quest for truly immersive communication.

Example-based super-resolution (SR) is an attractive option to Bayesian approaches to enhance image resolution. We use a multiresolution approach to example-based SR and discuss codebook construction for video sequences. We match a block to be super-resolved to a low-resolution version of the reference high-resolution image blocks. Once the match is found, we carefully apply the high-frequency contents of the chosen reference block to the one to be super-resolved. In essence, the method relies on “betting” that if the low-frequency contents of two blocks are very similar, their high-frequency contents also might match. In particular, we are interested in scenarios where examples can be picked up from readily available high-resolution images that are strongly related to the frame to be super-resolved. Hence, they constitute an excellent source of material to construct a dynamic codebook. Here, we propose a method to super-resolve a video using multiple overlapped variable-block-size codebooks. We implemented a mixed-resolution video coding scenario, where some frames are encoded at a higher resolution and can be used to enhance the other lower-resolution ones. In another scenario, we consider the framework where the camera captures a video at a lower resolution and also takes periodic snapshots at a higher resolution. Results indicate substantial gains over interpolation and fixed-codebook SR, and significant gains over previous works as well.

Efficient recording and replay of telemanipulation sessions require the compression of the occurring visual-haptic signals. To this end, we propose two distinct approaches which take into consideration known limitations of human visual and haptic perception. We compare both schemes to the state-of-the-art approaches and show that a substantial additional data reduction can be achieved while maintaining good overall haptic and visual performances.

The performance of haptic interaction across communication networks critically depends on the successful reconstruction of the bidirectionally transmitted haptic signals, and hence on the quality of the communication channel. We propose a novel error-resilient data reduction scheme for haptic communication which exploits known limits of human haptic perception. Particularly, we show that missing haptic information due to packet loss may strongly impair the user's experience during haptic interaction. We present and compare methods that eliminate the disturbing artifacts resulting out of packet loss. Our approach keeps the estimated impact of packet losses below human perception thresholds. A tree of possible cases (packets received or not received) and their respective occurrence probabilities is maintained at the sender side, and the system predicts unacceptable error cases to decide whether extra packets should be sent. We introduce different criteria that can be employed to trigger additional packets. In our experiments, we evaluate both the objective data reduction performance and the subjective system transparency by performing extensive tests using packet loss probability and round trip time as parameters. The proposed scheme shows excellent performances in terms of data reduction while sustaining good subjective ratings for a wide range of packet loss values and round trip times.

Many scalable video coding systems use frame down- sampling in order to reduce complexity and to enable enhancement layers. Super-resolution (SR) can be used to help the up-sampling and recovering processes of those frames. We are interested in reversed-complexity (distributed) coding methods, wherein few key frames are encoded at normal resolution, while the rest are down- sampled and encoded at reduced resolution along with the enhancement layers. We are only interested on the decoder side, wherein we carry motion estimation of the down- sampled frames using the key frames as references. When a match is made, the high-frequency components of the key frames (KF) are used to super-resolve the non-key frames (NKF). The motion estimation process is performed using blocks of band-pass versions of the frames, rather than low- pass ones. Results indicate the improved performance of the proposed super-resolution algorithm.

In many video compression systems, the frames are down-sampled before transmission. Also, in many scalable systems, the residual after down- and up-sampling is encoded and transmitted. Sometimes, a few frames are encoded at normal resolution (key frames) while the other frames are encoded at reduced resolution. Super resolution can be used to enhance the up-sampling process, using motion information to improve traditional interpolation. In this paper, we propose to use a super resolution method to up-sample the non-key frames using the key frames as reference. We build dictionaries on-the- fly using the key frames instead of the traditional off-line training images. The high-frequency data of matching blocks are added to the low-resolution blocks. Since the key frames are very similar to the non-key frames, the method is robust enough to allow successful super resolution of highly compressed (severely degraded) sequences. Results are presented for many predefined block sizes, key frames frequencies, and compression parameters.