Multi-rate encoding for HEVC-based adaptive HTTP streaming with multiple resolutions

Schroeder, Damien; Ilangovan, Adithyan; Steinbach, Eckehard

doi:10.1109/mmsp.2015.7340822

Cited by 9 publications

(7 citation statements)

References 8 publications

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“…For comparison, the latest multiple resolutions encoding algorithm [12] from literature is also evaluated using the same test settings. Although [12] is based on HEVC and best suited for low resolution encoding acceleration, it can be migrated to AV1 without difficulty and accelerate high resolution encoding. Table 3 shows the performance of this algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…This method resulted in considerable RD loss and was later improved by an RDO tree pruning algorithm [4] [5]. [12] proposed a heuristic multiple resolutions encoding algorithm for HEVC, where low resolution encoding is accelerated by a high resolution reference encoding. However, multiple resolutions encoding are often done in parallel, and the highest resolution takes the longest time.…”

Section: Related Workmentioning

confidence: 99%

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Fast Block Structure Determination in Av1-Based Multiple Resolutions Video Encoding

Guo

Han

Wen

2018

2018 IEEE International Conference on Multimedia and Expo (ICME)

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The widely used adaptive HTTP streaming requires an efficient algorithm to encode the same video to different resolutions. In this paper, we propose a fast block structure determination algorithm based on the AV1 codec that accelerates high resolution encoding, which is the bottle-neck of multiple resolutions encoding. The block structure similarity across resolutions is modeled by the fineness of frame detail and scale of object motions, this enables us to accelerate high resolution encoding based on low resolution encoding results. The average depth of a block's co-located neighborhood is used to decide early termination in the RDO process. Encoding results show that our proposed algorithm reduces encoding time by 30.1%-36.8%, while keeping BD-rate low at 0.71%-1.04%. Comparing to the state-of-the-art, our method halves performance loss without sacrificing time savings.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fast Block Structure Determination in Av1-Based Multiple Resolutions Video Encoding

Guo

Han

Wen

2018

2018 IEEE International Conference on Multimedia and Expo (ICME)

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“…The same idea has been extended in [50] to be used by dependent low-resolution representations. As CTUs do not contain the same area in different resolutions, the corresponding area ( ) for the current CTU in a dependent low-resolution representation is found in the reference representation.…”

Section: Multiratementioning

confidence: 99%

“…Ideas in [19] and [50] has been combined in [51] to introduce an algorithm that is used for both dependent lowquality and low-resolution representations.…”

Section: Multiratementioning

confidence: 99%

CTU depth decision algorithms for HEVC: A survey

Çetinkaya

Amirpour

Ghanbari

et al. 2021

Signal Processing: Image Communication

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High-Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The Coding Tree Unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64 × 64 pixels. Each CTU is then divided recursively into a number of equally sized square areas, known as Coding Units (CUs). Although this diversity of frame partitioning increases encoding efficiency, it also causes an increase in the time complexity due to the increased number of ways to find the optimal partitioning. To address this complexity, numerous algorithms have been proposed to eliminate unnecessary searches during partitioning CTUs by exploiting the correlation in the video. In this paper, existing CTU depth decision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches. Statistics approaches are further subdivided into neighboring and inherent approaches. Neighboring approaches exploit the similarity between adjacent CTUs to limit the depth range of the current CTU, while inherent approaches use only the available information within the current CTU. Machine learning approaches try to extract and exploit similarities implicitly. Traditional methods like support vector machines or random forests use manually selected features, while recently proposed deep learning methods extract features during training. Finally, this paper discusses extending these methods to more recent video coding formats such as Versatile Video Coding (VVC) and AOMedia Video 1 (AV1).

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“…However, these encoders focus on providing different versions of a video at different bit rates [10], spatial resolutions [11], or a combination of both [12]. Since these works only accelerate CU decisions, the computational complexity of the resulting encoder is still significantly higher compared to the complexity of a single encoder.…”

Section: Related Workmentioning

confidence: 99%

Multistream video encoder for generating multiple dynamic range bitstreams

Goethem

Praeter

Paridaens

et al. 2016

2016 Picture Coding Symposium (PCS)

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Abstract-High-dynamic-range (HDR) technology allows capturing of video content at a wider range of luminance than lowdynamic-range (LDR) video. The resulting video more closely resembles the scene as perceived by the human eye. However, displays currently support only a limited range of HDR. Therefore, both an HDR version and LDR version of a video should be encoded during content acquisition. This means that the cost of encoder hardware in cameras would double. As a solution, this paper proposes a multistream video encoder that allows generating an HDR and LDR version of the same HDR video footage at roughly the same computational complexity as a single encoder, effectively allowing encoding of two dynamicrange versions of the video with a negligible increase in cost. For the LDR version, this multistream encoder results in a bit rate overhead of only 11.6% for the same quality as a two-encoder solution.

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Multi-rate encoding for HEVC-based adaptive HTTP streaming with multiple resolutions

Cited by 9 publications

References 8 publications

Fast Block Structure Determination in Av1-Based Multiple Resolutions Video Encoding

Fast Block Structure Determination in Av1-Based Multiple Resolutions Video Encoding

CTU depth decision algorithms for HEVC: A survey

Multistream video encoder for generating multiple dynamic range bitstreams

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