FITD: Fast Intra Transcoding from H.264/AVC to high efficiency video coding based on DCT coefficients and prediction modes

Lin, Chow-Sing; Yang, Wein-Duo; Su, Chieh-Wei

doi:10.1016/j.jvcir.2016.03.003

Cited by 10 publications

(8 citation statements)

References 21 publications

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“…For example, tablets or mobile devices do not have enough capacity to display the high definition videos, for this purpose, high spatial videos are down sampled into low spatial videos and vice versa. For spatial downscaling, the discrete cosine transform (DCT) scheme has been shown significantly higher visual quality than the low-pass filtering schemes of pixel domain [12]. However, some applications need to show standard definition television (SDTV) bitsream on high definition mobile devices.…”

Section: A Backgroundmentioning

confidence: 99%

“…This technique did not perform well when noise ratio increased in the video frame. Chow-Sing et al [12] proposed a fast intra transcoding from H.264/AVC to high efficiency video coding based on discrete cosine transform (DCT) coefficients and prediction modes. This method uses intra prediction information embedded in H.264 bitstream as well as DCT coefficients to predict the coding depth map for depth limitation.…”

Section: Literature Reviewmentioning

confidence: 99%

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Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

Qureshi¹,

Nawaz²

2021

Preprint

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Conversion of one video bitstream to another video bitstream is a challenging task in the heterogeneous transcoder due to different video formats. In this paper, a region of interest (ROI) based super resolution technique is used to convert the lowresolution AVS (audio video standard) video to high definition HEVC (high efficiency video coding) video. Firstly, we classify a low-resolution video frame into small blocks by using visual characteristics, transform coefficients, and motion vector (MV) of a video. These blocks are further classified as blocks of most interest (BOMI), blocks of less interest (BOLI) and blocks of noninterest (BONI). The BONI blocks are considered as background blocks due to less interest in video and remains unchanged during SR process. Secondly, we apply deep learning based super resolution method on low resolution BOMI, and BOLI blocks to enhance the visual quality. The BOMI and BOLI blocks have high attention due to ROI that include some motion and contrast of the objects. The proposed method saves 20% to 30% computational time and obtained appreciable results as compared with full frame based super resolution method. We have tested our method on different official video sequences with resolution of 1K, 2K, and 4K. Our proposed method has an efficient visual performance in contrast to the full frame-based super resolution method.

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Section: A Backgroundmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

Qureshi¹,

Nawaz²

2021

Preprint

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“…It is worth mentioning that the majority of reported HEVC transcoders is related to H.264 to HEVC transcoding for both intra only and intra/inter modes. For instance, [14] proposed fast intra H.264 to HEVC transcoder by leveraging the incoming DCT coefficients and intra predictions to predict the coding depth in HEVC CUs. The transcoder is 1.7-2.5 times faster than ordinary HEVC encoding with a bitrate penalty up to 3%.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting split decisions in MPEG-2 to HEVC video transcoding

Shanableh

Hassan²

2020

SN Appl. Sci.

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This paper proposes learning-based approaches for transcoding videos compressed using the Moving Picture Experts Group 2 format into the High Efficiency Video Coding (HEVC) format. In the training mode of the transcoder, mappings between extracted features and split decisions are calculated. While in the transcoding mode, the split decisions of Coding Units of the HEVC video are predicted. Two formulations are proposed for the prediction of split decisions based on multi model and multi-tier solutions. In the former solution, multi models are generated based on the total number of split flags in a coding unit. While in the latter solution, split decisions are modelled at three different coding depths. The proposed solutions are evaluated in terms of excessive bitrate, drop in PSNR, classification accuracy, model generation time and transcoding speedup. It is shown that the multi-tier solution maintains the rate-distortion behaviour of full re-encoding at the expense of lower gain in transcoding speedup. In comparison to existing work, it is shown that the proposed solutions offer a significant enhancement in terms of rate-distortion performance and classification accuracy.

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“…In the above studies, H.264/AVC to HEVC transcoders mostly focus on inter‐transcoding. In [9], a fast intra‐transcoding from H.264/AVC to HEVC is proposed. The above‐mentioned schemes all use information extracted from H.264/AVC bit streams to form a mode mapping method between H.264/AVC and HEVC.…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based H.264/AVC to HEVC transcoding via motion information reuse and coding mode similarity analysis

Lin

Qing

et al. 2019

IET Image Processing

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FITD: Fast Intra Transcoding from H.264/AVC to high efficiency video coding based on DCT coefficients and prediction modes

Cited by 10 publications

References 21 publications

Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

Predicting split decisions in MPEG-2 to HEVC video transcoding

Machine learning‐based H.264/AVC to HEVC transcoding via motion information reuse and coding mode similarity analysis

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