Out-of-the-loop information hiding for HEVC video

Communication using internet and digital media is more and more popular. Therefore, the security and privacy of data transmission are highly demanded. One effective technique providing this requirement is information hiding. This technique allows to conceal secret information into a video file, an audio, or a picture. In this paper, we propose a low complexity out-of-the-loop information hiding algorithm for a video pre-encoded with the high efficiency video coding standard. Only selected components such as the motion vector difference and transform coefficients of the video are extracted and modified, bypassing the need of fully decoding and re-encoding the video. In order to reduce the propagation error caused by hiding information, the dependency between video frames is taken into account when distributing the information over the frame. Several embedding strategies are investigated. The experimental results show that the information should be hidden in smaller blocks to reduce quality loss. Using a smart distribution of information across the frames can keep the quality loss under 1 dB PSNR for an information payload of 15 kbps. When such a strategy is used, embedding information in the transform coefficients only slightly outperforms the modification of motion vector differences

Fast encoding for personalized views extracted from beyond high definition content

Broadcast providers are looking for new opportunities to increase user experience and user interaction on their content. Their main goal is to attract and preserve viewer attention to create a big and stable audience. This could be achieved with a second screen application that lets the users select their own viewpoint in an extremely high resolution video to direct their own first screen. By allowing the users to create their own personalized video stream, they become involved with the content creation itself. However, encoding a personalized view for each user is computationally complex. This paper describes a machine learning approach to speed up the encoding of each personal view. Simulation results of zoom, pan and tilt scenarios show bit rate increases between 2% and 9% for complexity reductions between 69% and 79% compared to full encoding

Efficient bit rate transcoding for high efficiency video coding

High efficiency video coding (HEVC) shows a significant advance in compression efficiency and is considered to be the successor of H.264/AVC. To incorporate the HEVC standard into real-life network applications and a diversity of other applications, efficient bit rate adaptation (transrating) algorithms are required. A current problem of transrating for HEVC is the high computational complexity associated with the encoder part of such a cascaded pixel domain transcoder. This paper focuses on deriving an optimal strategy for reducing the transcoding complexity with a complexity-scalable scheme. We propose different transcoding techniques which are able to reduce the transcoding complexity in both CU and PU optimization levels. At the CU level, CUs can be evaluated in top-to-bottom or bottom-to-top flows, in which the coding information of the input video stream is utilized to reduce the number of evaluations or to early terminate certain evaluations. At the PU level, the PU candidates are adaptively selected based on the probability of PU sizes and the co-located input PU partitioning. Moreover, with the use of different proposed methods, a complexity-scalable transrating scheme can be achieved. Furthermore, the transcoding complexity can be effectively controlled by the machine learning based approach. Simulations show that the proposed techniques provide a superior transcoding performance compared to the state-of-the-art related works. Additionally, the proposed methods can achieve a range of trade-offs between transrating complexity and coding performance. From the proposed schemes, the fastest approach is able to reduce the complexity by 82% while keeping the bitrate loss below 3%

Clinical efficacy of transcutaneous tibial nerve stimulation (TTNS) versus sham therapy (part I) and TTNS versus percutaneous tibial nerve stimulation (PTNS) (part II) on the short term in children with the idiopathic overactive bladder syndrome : protocol for part I of the twofold double-blinded randomized controlled TaPaS trial

Background Transcutaneous tibial nerve stimulation (TTNS) and percutaneous tibial nerve stimulation (PTNS) are effective and safe therapies for overactive bladder (OAB) syndrome in adults. However, few randomized sham-controlled trials have been conducted in a pediatric population. To our knowledge, both therapies never have been compared in children. Aim The aim of the complete study is twofold: (1) to assess the efficacy of TTNS therapy on bladder symptoms after 12 weeks of treatment in a pediatric population with idiopathic overactive bladder syndrome (iOAB) and/or nocturnal enuresis (part I) and (2) to assess the effect of TTNS compared to PTNS (part II). In this article, we aim to present the protocol of the first part of the TaPaS trial (TTNS, PTNS, sham therapy). Methods Part I of the TaPaS trial is set up as a single-center randomized-controlled trial. Children, aged from 5 to 12 years with iOAB and/or nocturnal enuresis, are assigned to two groups by computer-generated randomization: TTNS therapy (intervention) and sham therapy (control). The primary outcome is the percentage difference in average voided volume (AVV) between baseline and after 12 weeks of treatment. Secondary endpoints are the percentage difference in supervoid volumes, number of urinary incontinence episodes/24 h and in voiding frequency, the difference in parent reported outcomes between baseline and after 12 weeks of treatment, and the duration of clinical response. Discussion We hypothesize that TTNS is a non-inferior treatment for iOAB in children compared to PTNS therapy. Since literature is inconclusive about the efficacy of TTNS in a pediatric population, a sham-controlled RCT on TTNS will be conducted (part I). A protocol for a prospective randomized sham-controlled trial has been developed. Enrolment has started in November 2018. Study completion of part I is expected by August 2021