Optimum Encoding Approaches on Video Resolution Changes: A Comparative Study

Asan, Avsar; Mkwawa, Is-Haka; Sun, Lingfen; Robitza, Werner; Begen, Ali C.

doi:10.1109/icip.2018.8451635

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Finally, Wave One recently proposed video encoding with deep neural networks [32] and demonstrated quality gains against a conventional video encoder without B frames, and focusing on very-high bitrate encoding (20mbps or higher for FHD). While these are important achievements, most of these proposals are still outperformed by post-2013 video encoders, like HEVC and VP9, when utilized with their most advanced video buffering verifier (VBV) encoding configurations and appropriate constant rate factor tuning [33]. In addition, all these proposals require advanced GPU capabilities on both the client and the server side that cannot be supported by existing video players as they break away from current standards.…”

Section: Related Workmentioning

confidence: 99%

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Deep Video Precoding

Bourtsoulatze¹,

Chadha²,

Fadeev³

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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Several groups worldwide are currently investigating how deep learning may advance the state-of-the-art in image and video coding. An open question is how to make deep neural networks work in conjunction with existing (and upcoming) video codecs, such as MPEG H.264/AVC, H.265/HEVC, VVC, Google VP9 and AOMedia AV1, as well as existing container and transport formats, without imposing any changes at the client side. Such compatibility is a crucial aspect when it comes to practical deployment, especially when considering the fact that the video content industry and hardware manufacturers are expected to remain committed to supporting these standards for the foreseeable future.We propose to use deep neural networks as precoders for current and future video codecs and adaptive video streaming systems. In our current design, the core precoding component comprises a cascaded structure of downscaling neural networks that operates during video encoding, prior to transmission. This is coupled with a precoding mode selection algorithm for each independently-decodable stream segment, which adjusts the downscaling factor according to scene characteristics, the utilized encoder, and the desired bitrate and encoding configuration. Our framework is compatible with all current and future codec and transport standards, as our deep precoding network structure is trained in conjunction with linear upscaling filters (e.g., the bilinear filter), which are supported by all web video players. Results with FHD (1080p) and UHD (2160p) content and widelyused H.264/AVC, H.265/HEVC and VP9 encoders show that coupling such standards with the proposed deep video precoding allows for 15% to 45% rate reduction under encoding configurations and bitrates suitable for video-on-demand adaptive streaming systems. The use of precoding can also lead to encoding complexity reduction, which is essential for cost-effective cloud deployment of complex encoders like H.265/HEVC and VP9, especially when considering the prominence of high-resolution adaptive video streaming.

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Section: Related Workmentioning

confidence: 99%

“…Fig. 5a shows the set of RD points obtained after precoding the GOP for each scale factor in S and encoding with H.264/AVC via VBV encoding with the CRF values of Table I, which is representative of real-world streaming presets [33], and both maximum bitrate and buffer size set to 500kbps. As shown in Fig.…”

Section: Adaptive Video Precodingmentioning

confidence: 99%

Deep Video Precoding

Bourtsoulatze¹,

Chadha²,

Fadeev³

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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Performance evaluation of mpeg-5 part 2 (lcevc): impact of packet loss

Roberts,

Adeyemi-Ejeye

2024

Multimed Tools Appl

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This paper presents an evaluation of the latest MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) for video streaming applications using best effort protocols. LCEVC is a new video standard by MPEG, which enhances any base codec through an additional low bitrate stream, improving both video compression efficiency and and transmission. However, there is an interplay between packetization, packet loss visibility, choice of codec and video quality, which implies that prior studies with other codecs may be not as relevant. The contributions of this paper is, therefore in twofold: It evaluates the compression performance of LCEVC and then the impact of packet loss on its video quality when compared to H.264 and HEVC.The results from this evaluation suggest that, regarding compression, LCEVC outperformed its base codecs, overall in terms average encoding bitrate savings when using the constant rate factor (CRF) rate control. For example at a CRF of 19, the average encoding bitrate was reduced by 18.7% and 15.8% when compared with the base H.264 and HEVC codecs respectively. Furthermore, LCEVC produced better visual quality across the packet loss range compared to its base codecs and the quality only started to decrease once packet loss exceeded 0.8-1%, and decreases at a slower pace compared to its equivalent base codecs. This suggests that the LCEVC enhancement layer also provides error concealment. The results presented in this paper will be of interest to those considering the LCEVC standard and expected video quality in error-prone environments

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Optimum Encoding Approaches on Video Resolution Changes: A Comparative Study

Cited by 2 publications

References 12 publications

Deep Video Precoding

Deep Video Precoding

Performance evaluation of mpeg-5 part 2 (lcevc): impact of packet loss

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