Dominic Springer

Modeling the energy consumption of HEVC P- and B-frame decoding

Video decoding on portable devices such as smartphones or tablet PCs requires a considerable amount of battery power, shortening their operating time significantly. Hence, tools aiming at minimizing the decoding energy are of special interest. To this end, this paper presents a novel model capable of estimating the energy consumed when decoding an HEVC-coded video. This information can be used to optimize implementations and improve the encoding procedure. An accurate and a more convenient simple model is proposed that, for the evaluated set of test videos, achieved an average relative estimation error of 2.34% and 3.63%, respectively.

Modeling the energy consumption of HEVC intra decoding

Battery life is one of the major limitations to mobile device use, which makes research on energy efficient soft- and hardware an important task. This paper investigates the energy required by a CPU when decoding compressed bitstream videos on mobile platforms. A model is derived that describes the energy consumption of the new HEVC decoder for intra coded videos. We show that the relative estimation error of the model is smaller than 3.2% and that the model can be used to build encoders aiming at minimizing decoding energy.

Compression of 2D and 3D navigation video sequences using skip mode masking of static areas

In order to assert correct behavior of electronics in modern automobiles, extensive tests are conducted. Part of these tests focus on the correct rendering of the navigation systems, including map rotation, content of info boxes and smoothness of frame updates. Test engineers have the need to record the rendered navigation system in live setups (e.g. in moving cars) and evaluate them afterwards. Traditional video encoding produces significant bitrate overhead due to the rotating characteristics of the navigation since rotation is approximated with small macroblock partitioning. In this paper, we show how to construct an encoding scheme specifically designed for encoding of 2D and 3D navigation video sequences. For this purpose we develop a Global Motion Estimation (GME) based on feature matching and model parameter estimation and combine it with an H.264/AVC video encoder as backend. By using skip mode information from the H.264/AVC rate distortion optimization, we are able to stabilize the parameter estimation process even in the presence of large static areas.

Open source HEVC analyzer for rapid prototyping (HARP)

The design of new HEVC extensions comes with the need for careful analysis of internal HEVC codec decisions. Several bitstream analyzers have evolved for this purpose and provide a visualization of encoder decisions as seen from a decoder viewpoint. None of the existing solutions is able to provide actual insight into the encoder and its RDO decision process. With one exception, all solutions are closed source and make adaption of their code to specific implementation needs impossible. Overall, development with the HM code base remains a time-consuming task. Here, we present the HEVC Analyzer for Rapid Prototyping (HARP), which directly addresses the above issues and is freely available under www.lms.lnt.de/HARP.

Robust Rotational Motion Estimation for efficient HEVC compression of 2D and 3D navigation video sequences

In the context of test automation for automobiles, the compressed video recording of infotainment system components like navigation devices is a required practice. These recordings are then analyzed, archived, and forwarded to the responsible engineering teams. In order to compress navigation video sequences efficiently, the dominant rotational motion must be compensated properly. However, the process of Rotational Motion Estimation (RME) is hindered by the presence of static areas like info boxes and overlay graphics. We analyze this problem and show how to build masks for static areas in order to allow high speed feature transforms to be applied. With the acquired fast and accurate RME, we then demonstrate how to significantly reduce the required bitrate during HEVC encoding of navigation sequences.

Motion vector analysis based homography estimation for efficient HEVC compression of 2D and 3D navigation video sequences

Navigation systems have become complex devices in automobiles nowadays. As part of large in-car infotainment systems, these devices undergo extensive hardware and software tests in order to assert correct system behavior under all circumstances. During field tests, the display output is typically recorded in compressed form for days or weeks, followed by a thorough analysis of the video data. In this paper, we demonstrate how to setup an HEVC-based compression solution specifically designed for navigation sequence content. We show how rotational motion, which is a dominant characteristic, can be estimated and compensated in an efficient way. We avoid any complex feature-based approaches for global motion estimation but find precise motion parameters by analyzing and filtering motion vector sets produced by HEVC during encoding. While achieving compression efficiency similar to a feature-based approach, processing time for global motion estimation can be significantly reduced.

Modeling the Energy Consumption of the HEVC Decoding Process

In this paper, we present a bit stream feature-based energy model that accurately estimates the energy required to decode a given High Efficiency Video Coding-coded bit stream. Therefore, we take a model from literature and extend it by explicitly modeling the in-loop filters, which was not done before. Furthermore, to prove its superior estimation performance, it is compared with seven different energy models from the literature. By using a unified evaluation framework, we show how accurately the required decoding energy for different decoding systems can be approximated. We give thorough explanations on the model parameters and explain how the model variables are derived. To show the modeling capabilities in general, we test the estimation performance for different decoding software and hardware solutions, where we find that the proposed model outperforms the models from the literature by reaching framewise mean estimation errors of less than 7% for software and less than 15% for hardware-based systems.

Spiral search based fast rotation estimation for efficient HEVC compression of navigation video sequences

During the development and testing of navigation systems for modern cars, the video streams of the systems-under-test are carefully observed for errors of any kind. While this observation is typically carried out manually by software engineers monitoring the video output, the complexity and variety of the latest navigation systems demand an automated analysis of system functionality. Recently published work on automated error detection for navigation systems [1] can only enfold its full functionality if video sequences can be recorded and compressed in real-time, so that the full test program can be run under lab conditions after recording. However, the rotational motion of these sequences makes efficient compression a difficult task. In this paper, we present a fast and efficient method for rotation estimation, so that rotational motion compensation and thus efficient encoding can take place. Compared to existing state of the art approaches for SIFT- or SURF-based global motion estimation, our scheme requires 1/16th of the original processing time while providing almost identical quality gains of up to 2.5dB.

Compression of 2D navigation sequences with rotational and translational motion

In-car navigation systems have grown in complexity over the recent years, most notably in terms of route calculation, usability and graphical rendering. In order to guarantee correct system behavior, navigation systems need to be tested under real operating conditions, i.e. with field-tests on the road. In this paper, we will focus on a fast compression solution for 2D navigation renderings, so that field-tests can be archived and handed over to software engineers for subsequent evaluation. No parameters from the rendering procedure are available since access to the system is limited to the raw display signal. Rotation is a dominant factor throughout all navigation sequences, so we show how to reconstruct rotational motion parameters with high accuracy and develop a Global Motion Estimation (GME) method as support for a subsequent H.264/AVC video encoder. By integrating ratedistortion optimization concepts into our scheme, we can efficiently omit the segmentation of static and non-static areas. The runtime of the compression solution, which achieves bitrate savings of up to 19.5%, is evaluated both on a laptop CPU and an embedded OMAP4430 system on chip.

Real-time motion classification of HD video sequences on embedded systems

Fast local and global motion estimation (ME) is crucial for a wide variety of different video processing applications, but poses significant requirements on CPU resources. Interestingly, application scenarios shift more and more from traditional PC processing to embedded devices, mostly driven by home automation, machine vision and autonomous navigation. In this paper, we demonstrate how to use DSP capabilities on embedded ARM-based OMAP Systems-On-Chip (SoCs) to provide motion analysis up to HD at speeds of 15-30 fps. We show how to acquire reliable classification of motion types like zoom, rotation or translation, and how to calculate and decompose the so-called homography matrix for further refinement. Our focus lies on navigation sequences from automotive test scenarios, but we also demonstrate the accuracy of the approach on natural video sequences.