Multiple layers complexity allocation with dynamic control scheme for high-efficiency video coding

IF 2.9 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Real-Time Image Processing Pub Date : 2024-04-04 DOI:10.1007/s11554-024-01452-6

Jiunn-Tsair Fang, Ju-Kai Chen

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Abstract

High-efficiency video coding (HEVC) has significantly improved coding efficiency; however, its quadtree (QT) structures for coding units (CU) substantially raise the overall coding complexity. This study introduces a novel complexity control scheme aimed at enhancing HEVC encoding efficiency. The proposed scheme operates across multiple layers, encompassing the group of pictures (GOP) layer, frame layer, and coding-tree unit (CTU) layer. Each coding layer is assigned a limited coding complexity based on the remaining coding time. Particularly noteworthy is the dynamic scheme implemented to activate the complexity control method. To further expedite encoding, an efficient algorithm is developed for the CTU layer. Experimental results indicate that the 0.46% and 0.98% increases in BD-rate under the target complexity are reduced to 80% and 60% of the complexity constraint, respectively. The rate-distortion performance surpasses existing state-of-the-art complexity control methods, demonstrating the effectiveness of the proposed approach in enhancing HEVC encoding efficiency.

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高效视频编码的多层复杂性分配与动态控制方案

高效视频编码（HEVC）大大提高了编码效率；然而，其编码单元（CU）的四叉树（QT）结构大大提高了整体编码复杂度。本研究介绍了一种旨在提高 HEVC 编码效率的新型复杂度控制方案。建议的方案跨越多个层，包括图片组（GOP）层、帧层和编码树单元（CTU）层。每个编码层根据剩余编码时间分配有限的编码复杂度。尤其值得注意的是，为激活复杂度控制方法而实施的动态方案。为了进一步加快编码，还为 CTU 层开发了一种高效算法。实验结果表明，在目标复杂度下，BD 速率增加的 0.46% 和 0.98% 分别降低到了复杂度约束的 80% 和 60%。速率-失真性能超过了现有的最先进的复杂度控制方法，证明了所提出的方法在提高 HEVC 编码效率方面的有效性。

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来源期刊

Journal of Real-Time Image Processing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

6.80

自引率

6.70%

发文量

审稿时长

6 months

期刊介绍： Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed. Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application. It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system. The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.