An Empirical Measurement of the Coding Efficiency in Scalable Video Coding

the temporal correlation exists between the frame and the frame in a video message, in order to solve temporal redundancy.This paper adopts motion-compensated temporal filtering method for removing temporal correlation, motion-compensated temporal filtering and Haar wavelet lifting techniques are studied and discussed; finally presented the improved motion compensated temporal filtering methods. Results show the new method improves coding efficiency and scalability.

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Scalable Video Coding Bit Stream Extraction Based on Equivalent MSE Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.204-210.1728 ◽

2011 ◽

Vol 204-210 ◽

pp. 1728-1732 ◽

Cited By ~ 1

Author(s):

Da Xing Qian ◽

Hong Yu Wang ◽

Fang Lin Niu

Keyword(s):

Video Coding ◽

Scalable Video Coding ◽

Video Stream ◽

Frame Rate ◽

Scalable Video ◽

Enhancement Layer ◽

Coding Efficiency ◽

Frequency Components ◽

Bit Stream ◽

Spatial Enhancement

The bit stream extraction plays an important role in Scalable Video Coding (SVC) [1]. However, one downside of current video coding methods is to ignore the video contents which is in fact an important factor for video coding efficiency. Therefore, an equivalent MSE method is proposed in this paper to extract substreams in the temporal and spatial enhancement layers. When the Motion Vectors (MVs) are large in one video, a larger frame rate is necessary to maintain the continuity of the object movement which makes no jump in the visual sense. In this sense, substreams extraction in temporal enhancement layer has to be satisfied. On the other hand, if there are some larger high-frequency components in a single frame of the video, that is to say, there are some higher spatial details in the video stream. As a result, it should try to meet the extraction requirement in spatial enhancement layer. This method has the advantage of considering the contents of the video, which can effectively improve the coding performance and quality. The experimental results have demonstrated the improved quality of reconstructed video for the equivalent MSE method when extracting bit stream arbitrarily at the same bandwidth.

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Efficient Enhancement for Spatial Scalable Video Coding Transmission

Mathematical Problems in Engineering ◽

10.1155/2017/8239491 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13

Author(s):

Mayada Khairy ◽

Alaa Hamdy ◽

Amr Elsayed ◽

Hesham Farouk

Keyword(s):

Video Coding ◽

Video Compression ◽

Scalable Video Coding ◽

Standard Technique ◽

Base Layer ◽

Scalable Video ◽

Video Streams ◽

Coding Efficiency ◽

Advanced Video Coding ◽

Second Category

Scalable Video Coding (SVC) is an international standard technique for video compression. It is an extension of H.264 Advanced Video Coding (AVC). In the encoding of video streams by SVC, it is suitable to employ the macroblock (MB) mode because it affords superior coding efficiency. However, the exhaustive mode decision technique that is usually used for SVC increases the computational complexity, resulting in a longer encoding time (ET). Many other algorithms were proposed to solve this problem with imperfection of increasing transmission time (TT) across the network. To minimize the ET and TT, this paper introduces four efficient algorithms based on spatial scalability. The algorithms utilize the mode-distribution correlation between the base layer (BL) and enhancement layers (ELs) and interpolation between the EL frames. The proposed algorithms are of two categories. Those of the first category are based on interlayer residual SVC spatial scalability. They employ two methods, namely, interlayer interpolation (ILIP) and the interlayer base mode (ILBM) method, and enable ET and TT savings of up to 69.3% and 83.6%, respectively. The algorithms of the second category are based on full-search SVC spatial scalability. They utilize two methods, namely, full interpolation (FIP) and the full-base mode (FBM) method, and enable ET and TT savings of up to 55.3% and 76.6%, respectively.

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On the Coding Efficiency of MCTF Update Steps for Scalable Video Coding

IEEE Transactions on Circuits and Systems for Video Technology ◽

10.1109/tcsvt.2007.896646 ◽

2007 ◽

Vol 17 (6) ◽

pp. 779-784 ◽

Cited By ~ 3

Author(s):

Fengling Li ◽

Nam Ling ◽

Stephen A. Chiappari

Keyword(s):

Video Coding ◽

Scalable Video Coding ◽

Scalable Video ◽

Coding Efficiency

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Recent Advances in Peer-to-Peer Video Streaming by Using Scalable Video Coding

Streaming Media with Peer-to-Peer Networks ◽

10.4018/978-1-4666-1613-4.ch008 ◽

2012 ◽

pp. 162-195 ◽

Cited By ~ 1

Author(s):

Dan Grois ◽

Ofer Hadar

Keyword(s):

Video Coding ◽

Video Streaming ◽

Scalable Video Coding ◽

Peer To Peer ◽

Peer Networks ◽

Scalable Video ◽

End User ◽

Coding Efficiency ◽

Peer To Peer Networks ◽

The Media

Scalable Video Coding provides important functionalities, such as the spatial, temporal, and SNR (quality) scalability, thereby significantly improving coding efficiency over prior standards such as the H.264/AVC and enabling the power adaptation. In turn, these functionalities lead to the enhancement of the video streaming over Peer-to-Peer networks, thereby providing a powerful platform for a variety of multimedia streaming applications, such as video-on-demand, video conferencing, live broadcasting, and many others. P2P systems are considered to be extremely cost-effective, since they utilize resources of the peer machines (e.g., CPU resources, memory resources, and bandwidth). However, since bandwidth is usually not constant and also since Peer-to-Peer networks suffer from the packet loss, there is no guarantee for the end-user video presentation quality. In addition, due to different server and end-user hardware configurations, it will be useful to specify the quality of the media (e.g., the bit-rate, spatial/temporal resolution, and the like). As a result, the Scalable Video Coding approach is an excellent choice, since the media streaming can be adjusted to a suitable stream to fit a particular Peer-to-Peer network and particular end-user requirements.

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