Real-time model-based video stabilization for microaerial vehicles

The emerging branch of micro aerial vehicles (MAVs) has attracted a great interest for their indoor navigation capabilities, but they require a high quality video for tele-operated or autonomous tasks. A common problem of on-board video quality is the effect of undesired movements, so different approaches solve it with both mechanical stabilizers or video stabilizer software. Very few video stabilizer algorithms in the literature can be applied in real-time but they do not discriminate at all between intentional movements of the tele-operator and undesired ones. In this paper, a novel technique is introduced for real-time video stabilization with low computational cost, without generating false movements or decreasing the performance of the stabilized video sequence. Our proposal uses a combination of geometric transformations and outliers rejection to obtain a robust inter-frame motion estimation, and a Kalman filter based on an ANN learned model of the MAV that includes the control action for motion intention estimation.Peer ReviewedPostprint (author's final draft

Subjective Quality Assessment of the Impact of Buffer Size in Fine-Grain Parallel Video Encoding

Fine-Grain parallelism is essential for real-time video encoding performance. This usually implies setting a fixed buffer size for each encoded block. The choice of this parameter is critical for both performance and hardware cost. In this paper we analyze the impact of buffer size on image subjective quality, and its relation with other encoding parameters. We explore the consequences on visual quality, when minimizing buffer size to the point of causing the discard of quantized coefficients for highest frequencies. Finally, we propose some guidelines for the choice of buffer size, that has proven to be heavily dependent, in addition to other parameters, on the type of sequence being encoded. These guidelines are useful for the design of efficient realtime encoders, both hardware and software

Automatic facial analysis for objective assessment of facial paralysis

Facial Paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann Scale. Experiments show the Radial Basis Function (RBF) Neural Network to have superior performance

Is It Safe to Uplift This Patch? An Empirical Study on Mozilla Firefox

In rapid release development processes, patches that fix critical issues, or implement high-value features are often promoted directly from the development channel to a stabilization channel, potentially skipping one or more stabilization channels. This practice is called patch uplift. Patch uplift is risky, because patches that are rushed through the stabilization phase can end up introducing regressions in the code. This paper examines patch uplift operations at Mozilla, with the aim to identify the characteristics of uplifted patches that introduce regressions. Through statistical and manual analyses, we quantitatively and qualitatively investigate the reasons behind patch uplift decisions and the characteristics of uplifted patches that introduced regressions. Additionally, we interviewed three Mozilla release managers to understand organizational factors that affect patch uplift decisions and outcomes. Results show that most patches are uplifted because of a wrong functionality or a crash. Uplifted patches that lead to faults tend to have larger patch size, and most of the faults are due to semantic or memory errors in the patches. Also, release managers are more inclined to accept patch uplift requests that concern certain specific components, and-or that are submitted by certain specific developers.Comment: In proceedings of the 33rd International Conference on Software Maintenance and Evolution (ICSME 2017

Quality Assessment of Mobile Phone Video Stabilization

Smartphone cameras are used more than ever for photography and videography. This has driven mobile phone manufacturers to develop and enhance cameras in their mobile phones. While mobile phone cameras have evolved a lot, many aspects of the mobile phone camera still have room for improvement. One is video stabilization which aims to remove unpleasant motion and artifacts from video. Many video stabilization methods for mobile phones exist. However, there is no standard video stabilization quality assessment (VSQA) framework for comparing the performance of the video stabilization methods. Huawei wanted to improve the video stabilization quality of their mobile phones by investigating video stabilization quality assessment. As a part of that endeavor, this work studies existing VSQA frameworks found in the literature and incorporates some of their ideas into a VSQA framework established in this work. The new VSQA framework consists of a repeatable laboratory environment and objective sharpness and motion metrics. To test the VSQA framework, videos were captured on multiple mobile phones in the laboratory environment. These videos were first subjectively evaluated to find issues that are noticeable by humans. Then the videos were objectively evaluated with the objective sharpness and motion metrics. The results show that the proposed VSQA framework can be used for comparing and ranking mobile devices. The VSQA framework successfully identifies the strengths and weaknesses of each tested device's video stabilization quality.Älypuhelimien kameroita käytetään nykyään valokuvaukseen enemmän kuin koskaan. Tämä on saanut älypuhelimien valmistajia kehittämään heidän puhelimiensa kameroita. Vaikka paljon edistystä on tapahtunut, niin moni älypuhelimen kameran osa-alueista kaipaa vielä kehitystä. Yksi heikoista osa-alueista on videostabilointi. Videostabiloinnin tarkoitus on poistaa videosta epämiellyttävä liike. Monia ratkaisuja löytyy, mutta mitään standardoitua tapaa vertailla eri stabilointi ratkaisuja ei ole. Huawei haluaa parantaa tuotteidensa videostabiloinnin laatua. Saavuttaakseen tämän tavoitteen, tässä työssä tehdään katsaus kirjallisuudesta löytyviä videostabiloinnin laadun mittausmenetelmiä ja jalostetaan näistä ideoita, joiden avulla kehitetään oma videonstabiloinnin laadun mittausmenetelmä. Menetelmä koostuu toistettavasta laboratorioympäristöstä, jossa voi kuvata heiluvia videoita eri älypuhelimilla. Näitä videoita vertaillaan objektiivisesti mittaamalla videoista terävyyttä ja liikkeen miellyttävyyttä. Työn videostabiloinnin laadun mittausmenetelmää testattiin kuvaamalla toistettavassa laboratorioympäristössä usealla älypuhelimella videoita, joissa on simuloitua käden tärinää. Ensin kuvattuja videoita arvioitiin ja vertailtiin subjektiivisesti, jotta niistä löytyisi ongelmat, joita videostabilointi ei ole onnistunut korjaamaan. Tämän jälkeen videoita arvioitiin objektiivisilla terävyys- ja liikemittareilla. Tulokset osoittavat, että työssä esitetty videostabiloinnin laadun mittausmenetelmää voidaan käyttää eri älypuhelimien videostabilointimenetelmien vertailuun. Työn mittausmenetelmä onnistui havaitsemaan eri video stabilointimenetelmien vahvuudet ja heikkoudet