Study and implementation of K-multiple constraint shortest path for H.265 HEVC for optimal video compression

Abstract

Today, everyone is demanding high-quality video services along with large bandwidth. Due to this demand, a ruthless burden is felt across the network architecture. Thus, a recently developed video codec H.265, also called high-efficiency video coding (HEVC), offers high coding efficiency than its predecessor H.264 Advanced Video Coding. Although H.265 or HEVC offers a greatly improved compression ratio, it is done at the expense of a high increase in computational complexity and time complexity to process the video compression. This disadvantage of HEVC makes it less suitable for real-time video sequences that may contain varying motion activities. Therefore, it is insufficient to use conventional motion estimation techniques that consume 80–90% of the total computational power. The research work presented in this paper deals with these two-short coming of H.265 HEVC. In this paper, first, an improved motion estimation technique is presented, which yields higher compression efficiency, computational complexities and better video quality, especially for low and medium video resolution applications such as handheld devices based videotelephony. Experimental results using improved motion estimation technique based on Quadtree decomposition and A* prune algorithm optimization reveals that total bits for different multi constraints shortest path get reduced while retaining the quality of video in reconstruction part. Besides, it can also be used in various multimedia applications with restricted computational resources.