HEVC optimization Research Papers - Academia.edu (original) (raw)

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Video compression plays a vital part in many digital video processing for applications such as digital video transmission, also thousands of websites like YouTube, Netflix etc. that requires large storage space. Video compression technologies are about reducing and removing redundant video data so that a digital video file can be effectively sent over a network or can be put in storage on computer disks with the reduction of data size. In this research paper video compression using motion compensation technique that reduces video data based on motion estimation from one frame to another, is proposed. Diamond Search (DS) motion compensation is an algorithmic technique employed for the encoding of video data for video compression. Motion compensation vectors describe a frame in terms of the transformation of a reference frame with respect to the current frame. The reference frame may be previous in time or even from the future. The proposed method reduces the searching of compression portion based on DS Algorithm in temporal redundancy video sequences. Motion blocks are further compressed by using the Scalable Video Compression methods, Adaptive Dual Tree Complex Wavelet Transform (ADT-CWT) and SPIHT. The performance of the proposed methodology is evaluated in terms of the peak signal-to-noise ratio (PSNR) and the compression ratio (CR).

As the concept of quantization matrix becomes an important feature in recent video CODECs, an optimized quantization matrix is being considered in the High-Efficiency Video Coding (HEVC) standard. This paper describes the entropy encoding by familiarizing optimized quantization matrix, and so higher rate of compression can be accomplished over the improved entropy encoding. Experiments show that for the eight benchmark video sequences and PSNR for varying rate of data transmission is explored. Comparative analysis is made with the improved (WE-Encoding) and standard entropy encoding based on the performance measurements. The simulation results show that the proposed method (WE-OQM) can save the originality of the decoded video sequence far better even though the compression rate is increased. In addition, the overall analysis states that the proposed method is 35.29% better than the Standard Encoding and 62.5% better than the WE-Encoding. Ó 2017 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

This paper presents a novel scheme for light field compression
based on a randomize hierarchical multi-view extension
of high efficiency video coding (dubbed as RH-MVHEVC).
Specifically, a light field data are arranged as a multiple pseudotemporal
video sequences which are efficiently compressed
withMV-HEVC encoder, following an integrated random coding
technique and hierarchical prediction scheme. The critical
advantage of proposed RH-MVHEVC scheme is that it
utilizes not just a temporal and inter-view prediction, but efficiently
exploits the strong intrinsic similarities within each
sub-aperture image and among neighboring sub-aperture images
in both horizontal and vertical directions. Experimental
results consistently outperform the state-of-the-art compression
methods on benchmark ICME 2016 and ICIP 2017 grand
challenge data sets. It achieves an average up to 33.803% BDrate
reduction and 1.7978 dB BD-PSNR improvement compared
with an advanced JEM video encoder, and an average
20.4156% BD-rate reduction and 2.0644 dB BD-PSNR improvement
compared with a latest image-based JEM-anchor
coding scheme.

HEVC optimization Research Papers - Academia.edu (original) (raw)

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