Computer Science

This paper analyses the application of no-reference metric NIQE to the task of video-codec comparison. A number of issues in the metric behaviour on videos was detected and described. The metric has outlying scores on black and solid-coloured frames. The proposed averaging technique for metric quality scores helped to improve the results in some cases. Also, NIQE has low-quality scores for videos with detailed textures and higher scores for videos of lower bitrates due to the blurring of these textures after compression. Although NIQE showed natural results for many tested videos, it is not universal and currently can not be used for video-codec comparisons.

We present a data-driven basketball set play simulation. Given an offensive set play sketch, our method simulates potential scenarios that may occur in the game. The simulation provides coaches and players with insights on how a given set play can be executed. To achieve the goal, we train a conditional adversarial network on NBA movement data to imitate the behaviors of how players move around the court through two major components: a generator that learns to generate natural player movements based on a latent noise and a user sketched set play; and a discriminator that is used to evaluate the realism of the basketball play. To improve the quality of simulation, we minimize 1.) a dribbler loss to prevent the ball from drifting away from the dribbler; 2.) a defender loss to prevent the dribbler from not being defended; 3.) a ball passing loss to ensure the straightness of passing trajectories; and 4) an acceleration loss to minimize unnecessary players' movements. To evaluate our system, we objectively compared real and simulated basketball set plays. Besides, a subjective test was conducted to judge whether a set play was real or generated by our network. On average, the mean correct rates to the binary tests were 56.17 \%. Experiment results and the evaluations demonstrated the effectiveness of our system.

Storage systems often rely on multiple copies of the same compressed data, enabling recovery in case of binary data errors, of course, at the expense of a higher storage cost. In this paper we show that a wiser method of duplication entails great potential benefits for data types tolerating approximate representations, like images and videos. We propose a method to produce a set of distinct compressed representations for a given signal, such that any subset of them allows reconstruction of the signal at a quality depending only on the number of compressed representations utilized. Essentially, we implement the holographic representation idea, where all the representations are equally important in refining the reconstruction. Here we propose to exploit the shift sensitivity of common compression processes and generate holographic representations via compression of various shifts of the signal. Two implementations for the idea, based on standard compression methods, are presented: the first is a simple, optimization-free design. The second approach originates in a challenging rate-distortion optimization, mitigated by the alternating direction method of multipliers (ADMM), leading to a process of repeatedly applying standard compression techniques. Evaluation of the approach, in conjunction with the JPEG2000 image compression standard, shows the effectiveness of the optimization in providing compressed holographic representations that, by means of an elementary reconstruction process, enable impressive gains of several dBs in PSNR over exact duplications.

Indian Classical Dance is an over 5000 years' old multi-modal language for expressing emotions. Preservation of dance through multimedia technology is a challenging task. In this paper, we develop a system to generate a parseable representation of a dance performance. The system will help to preserve intangible heritage, annotate performances for better tutoring, and synthesize dance performances. We first attempt to capture the concepts of the basic steps of an Indian Classical Dance form, named Bharatanatyam Adavus, in an ontological model. Next, we build an event-based low-level model that relates the ontology of Adavus to the ontology of multi-modal data streams (RGB-D of Kinect in this case) for a computationally realizable framework. Finally, the ontology is used for transcription into Labanotation. We also present a transcription tool for encoding the performances of Bharatanatyam Adavus to Labanotation and test it on our recorded data set. Our primary aim is to document the complex movements of dance in terms of Labanotation using the ontology.

During Stereoscopic 3D (S3D) video transmission, one or both views can be affected by bit errors and packet losses caused by adverse channel conditions, delay or jitter. Typically, the Human Visual System (HVS) is incapable of aligning and fusing stereoscopic content if one view is affected by artefacts caused by compression, transmission and rendering with distorted patterns being perceived as alterations of the original which presents a shimmering effect known as binocular rivalry and is detrimental to a user's Quality of Experience (QoE). This study attempts to quantify the effects of binocular rivalry for stereoscopic videos. Existing approaches, in which one or more frames are lost in one or both views undergo error concealment, are implemented. Then, subjective testing is carried out on the error concealed 3D video sequences. The evaluations provided by these subjects were then combined and analysed using a standard Student t-test thus quantifying the impact of binocular rivalry and allowing the impact to be compared with that of monocular viewing. The main focus is implementing error-resilient video communication, avoiding the detrimental effects of binocular rivalry and improving the overall QoE of viewers.

Stereoscopic image quality measurement (SIQM) has become increasingly important for guiding stereo image processing and commutation systems due to the widespread usage of 3D contents. Compared with conventional methods which are relied on hand-crafted features, deep learning oriented measurements have achieved remarkable performance in recent years. However, most existing deep SIQM evaluators are not specifically built for stereoscopic contents and consider little prior domain knowledge of the 3D human visual system (HVS) in network design. In this paper, we develop a Predictive Auto-encoDing Network (PAD-Net) for blind/No-Reference stereoscopic image quality measurement. In the first stage, inspired by the predictive coding theory that the cognition system tries to match bottom-up visual signal with top-down predictions, we adopt the encoder-decoder architecture to reconstruct the distorted inputs. Besides, motivated by the binocular rivalry phenomenon, we leverage the likelihood and prior maps generated from the predictive coding process in the Siamese framework for assisting SIQM. In the second stage, quality regression network is applied to the fusion image for acquiring the perceptual quality prediction. The performance of PAD-Net has been extensively evaluated on three benchmark databases and the superiority has been well validated on both symmetrically and asymmetrically distorted stereoscopic images under various distortion types.

Deep Neural Networks have created a paradigm shift in our ability to comprehend raw data in various important fields ranging from computer vision and natural language processing to intelligence warfare and healthcare. While DNNs are increasingly deployed either in a white-box setting where the model internal is publicly known, or a black-box setting where only the model outputs are known, a practical concern is protecting the models against Intellectual Property (IP) infringement. We propose BlackMarks, the first end-to-end multi-bit watermarking framework that is applicable in the black-box scenario. BlackMarks takes the pre-trained unmarked model and the owner's binary signature as inputs and outputs the corresponding marked model with a set of watermark keys. To do so, BlackMarks first designs a model-dependent encoding scheme that maps all possible classes in the task to bit '0' and bit '1' by clustering the output activations into two groups. Given the owner's watermark signature (a binary string), a set of key image and label pairs are designed using targeted adversarial attacks. The watermark (WM) is then embedded in the prediction behavior of the target DNN by fine-tuning the model with generated WM key set. To extract the WM, the remote model is queried by the WM key images and the owner's signature is decoded from the corresponding predictions according to the designed encoding scheme. We perform a comprehensive evaluation of BlackMarks's performance on MNIST, CIFAR10, ImageNet datasets and corroborate its effectiveness and robustness. BlackMarks preserves the functionality of the original DNN and incurs negligible WM embedding runtime overhead as low as 2.054%.

Nowadays, with the development of public network usage, medical information is transmitted throughout the hospitals. The watermarking system can help for the confidentiality of medical information distributed over the internet. In medical images, regions-of-interest (ROI) contain diagnostic information. The watermark should be embedded only into non-regions-of-interest (NROI) to keep diagnostic information without distortion. Recently, ROI based watermarking has attracted the attention of the medical research community. The ROI map can be used as an embedding key for improving confidentiality protection purposes. However, in most existing works, the ROI map that is used for the embedding process must be sent as side-information along with the watermarked image. This side information is a disadvantage and makes the extraction process non-blind. Also, most existing algorithms do not recover NROI of the original cover image after the extraction of the watermark. In this paper, we propose a framework for blind diagnostically-lossless watermarking, which iteratively embeds only into NROI. The significance of the proposed framework is in satisfying the confidentiality of the patient information through a blind watermarking system, while it preserves diagnostic/medical information of the image throughout the watermarking process. A deep neural network is used to recognize the ROI map in the embedding, extraction, and recovery processes. In the extraction process, the same ROI map of the embedding process is recognized without requiring any additional information. Hence, the watermark is blindly extracted from the NROI.

Due to the recent demand of 3-D models in several applications like medical imaging, video games, among others, the necessity of implementing 3-D mesh watermarking schemes aiming to protect copyright has increased considerably. The majority of robust 3-D watermarking techniques have essentially focused on the robustness against attacks while the imperceptibility of these techniques is still a real issue. In this context, a blind robust 3-D mesh watermarking method based on mesh saliency and Quantization Index Modulation (QIM) for Copyright protection is proposed. The watermark is embedded by quantifying the vertex norms of the 3-D mesh using QIM scheme since it offers a good robustness-capacity tradeoff. The choice of the vertices is adjusted by the mesh saliency to achieve watermark robustness and to avoid visual distortions. The experimental results show the high imperceptibility of the proposed scheme while ensuring a good robustness against a wide range of attacks including additive noise, similarity transformations, smoothing, quantization, etc.

We propose a new Movie Map system, with an interface for exploring cities. The system consists of four stages; acquisition, analysis, management, and interaction. In the acquisition stage, omnidirectional videos are taken along streets in target areas. Frames of the video are localized on the map, intersections are detected, and videos are segmented. Turning views at intersections are subsequently generated. By connecting the video segments following the specified movement in an area, we can view the streets better. The interface allows for easy exploration of a target area, and it can show virtual billboards of stores in the view. We conducted user studies to compare our system to the GSV in a scenario where users could freely move and explore to find a landmark. The experiment showed that our system had a better user experience than GSV.