دانلود رایگان ترجمه مقاله تخمین حرکت سریع بر اساس ویژگی محتوا برای کدگذار H.265/HEVC با پیچیدگی کم (نشریه IEEE 2016)

تخمین حرکت سریع بر اساس ویژگی محتوا برای کدگذار H.265/HEVC با پیچیدگی کم

Fast Motion Estimation Based on Content Property for Low-Complexity H.265/HEVC Encoder

Abstract

The high definition (HD) and ultra HD videos can be widely applied in broadcasting applications. However, with the increased resolution of video, the volume of the raw HD visual information data increases significantly, which becomes a challenge for storage, processing, and transmitting the HD visual data. The state-of-the-art video compression standard-H.265/High Efficiency Video Coding (HEVC) compresses the raw HD visual data efficiently, while the high compression rate comes at the cost of heavy computation load. Hence, reducing the encoding complexity becomes vital for the H.265/HEVC encoder to be used in broadcasting applications. In this paper, based on the best motion vector selection correlation among the different size prediction modes, we propose a fast motion estimation (ME) method to reduce the encoding complexity of the H.265/HEVC encoder. First, according to the prediction unit (PU) partition type, all PUs are classified into two classes, parent PU and children PUs, respectively. Then, based on the best motion vector selection correlation between the parent PU and children PUs, the block matching search process of the children PUs is adaptively skipped if their parent PU chooses the initial search point as its final optimal motion vector in the ME process. Experimental results show that the proposed method achieves an average of 20% ME time saving as compared with the original HM-TZSearch. Meanwhile, the rate distortion performance degradation is negligible.

1 Introduction

T HE HIGH definition (HD) and ultra HD videos have emerged in response to the developments in video capture and display technologies, which have been widely used in security surveillance, ultra HD television system, and so on [1]. However, with the increased resolution of video, the volume of the raw HD visual information data increases significantly. This becomes a challenge for storage, processing and transmitting the visual data due to the current storage, computing, and transmission capability are still limited. Hence, the high compression rate and low complexity are the key requirements for the HD videos to be widely applied in broadcasting applications. Recently, the joint collaborative team on video coding (JCT-VC) under the ITU-T video coding experts group (VCEG) and ISO/IEC moving picture experts group (MPEG) launched a state-of-the-art video compression standard called H.265/high efficiency video coding (HEVC) [2], [3]. Compared to the H.264/advanced video coding (AVC) which is the previous generation video compression standard [4], the H.265/HEVC achieves about 50% bit rate saving while maintaining the same subjective visual quality. However, the higher compression rate comes at the cost of heavy computational complexity of a series of advanced coding tools used in the H.265/HEVC encoder, such as quadtree structure based coding unit (CU), large and asymmetric interframe/intraframe prediction unit (PU), and so on. Hence, reducing the encoding complexity becomes vital for the H.265/HEVC encoder to be widely used in broadcasting applications.

In order to reduce the encoding complexity of the H.265/HEVC encoder, many researchers have focused on optimizing the H.265/HEVC interframe prediction process [5]–[10]. Pan et al. [5] proposed a fast CU size decision method by using the CU quadtree depth selection correlation between the current CU and its spatial and temporal neighboring CUs. Since the CU size highly depends on the content of the CU, Shen et al. [6] proposed a fast CU size decision by using the correlation between the CU size and the CU content. Zhang et al. [7] proposed a machine learning based fast CU size decision method, which optimized the encoding complexity with acceptable rate distortion (RD) performance degradation. Based on the CU motion activity and hierarchial depth correlation, Pan et al. [8] proposed an early Merge/Skip mode decision for reducing the encoding complexity of PU encoding process. By using the CU inter-level and spatiotemporal correlations, Shen et al. [9] proposed an adaptive inter mode decision for reducing the complexity of the H.265/HEVC. Based on the spatiotemporal encoding parameters of the H.265/HEVC encoder, Ahn et al. [10] proposed a fast CU encoding method for H.265/HEVC inter coding. These methods can efficiently reduce the computational complexity of the interframe prediction process, however, the encoding complexity of the interframe prediction mainly comes from the motion estimation (ME) process, the encoding complexity of H.265/HEVC encoder can be further reduced by optimizing the ME process.