Convolutional Neural Network-Based Arithmetic Coding for HEVC Intra-Predicted Residues

Ma, Changyue; Liu, Dong; Peng, Xiulian; Li, Li; Wu, Feng

doi:10.1109/tcsvt.2019.2927027

Cited by 24 publications

(7 citation statements)

References 18 publications

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“…The criterion for deciding between two paths is to select the one having the smallest metric. This method maximizes the probability of a correct decision [25]. The Viterbi decoder consists of four main processes: branch metric computation, state metric update, survivor path recording and output decision generation [26].…”

Section: The Viterbi Algorithmmentioning

confidence: 99%

Improving bit error-rate based on adaptive Bose-Chaudhuri Hocquenghem concatenated with convolutional codes

Abdelshafy.

Hassan²,

Zakaria³

2021

IJEECS

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Several algorithms have been proposed to avoid the error floor region, such as the concatenation codes that requires high computational demands in addition to high complexity. This paper proposes a technique based on using cascaded BCH and convolutional codes that leads to better error correction performance. Moreover, an adaptive method based on sensing the channel's noise to determine the number of the parity bits that will be added to the used BCH that reduces the consumed bandwidth and the transmitted parity bits is presented. A further enhancement is fulfilled by using parallel processing branches, resulting in reducing the consumed time and speed up the performance. The results show that the proposed code presents a better performance. A high reduction in the number of cycles that will be used in the encoding and decoding compared with the classical method and finally a flexible parity bits method based on the signal-to-noise ratio of the channel that reduced the parity bits which leads to reduce the consumed bandwidth. The MATLAB simulation and the field programmable gate array (FPGA) implementation will be provided in this paper to validate the proposed concept.

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Section: The Viterbi Algorithmmentioning

confidence: 99%

Improving bit error-rate based on adaptive Bose-Chaudhuri Hocquenghem concatenated with convolutional codes

Abdelshafy.

Hassan²,

Zakaria³

2021

IJEECS

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“…For the transform and entropy coding, Liu et al [23] trained a DCT-like transform network for image coding. [24], [25] proposed using CNN to predict the probability distribution of the intra prediction related syntax elements. Reinforcement learning has been introduced into the traditional rate control to allocate bitrate and estimate coding parameters in [26], [27].…”

Section: B Deep Learning Based Video Codingmentioning

confidence: 99%

“…The second category methods still follow the conventional block based hybrid video coding framework [19]. The deep neural network is integrated into the framework to improve the performance of particular module including inter prediction [20]- [22], transform [23], entropy coding [24], [25], rate control [26], [27], in-loop filtering/post processing [28]- [31] and intra prediction [32]- [38] The details of these deep learning based video coding methods are reviewed in Section. II-B.…”

Section: Introductionmentioning

confidence: 99%

Current Frame Priors Assisted Neural Network for Intra Prediction

et al. 2021

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Intra prediction is the key technology to reduce spatial redundancy in the modern video coding standard. Recently, deep learning based methods that directly generate the intra prediction by neural network achieve superior performance than traditional directional based intra prediction. However, these methods lack the ability to handle complex blocks which contain mixed directional textures or recurrent patterns since they only use the neighboring reference samples of the current one. The other intermediate information denoted as reference priors in this paper generated during the coding process is not exploited. In this paper, a Current Frame Priors assisted Neural Network (CFPNN) is presented to improve the intra prediction efficiency. Specifically, we utilize the local contextual information provided by the neighboring multiple references as the primary inference source. In addition to the neighboring references, we additionally use the other two reference priors within the current frame -the predictor searched by intra block copy (IntraBC) and the corresponding residual component. The IntraBC predictor provides useful nonlocal information to help generate more accurate prediction for complex blocks together with neighboring local information. While the residual component contains unique information that reflects the characteristics of the block to some extent is utilized to reduce the noise contained in the reconstructed reference samples. Moreover, we investigate the best way to integrate the proposed method into the codec. Experimental results demonstrate that compared to HEVC, our proposed CFPNN achieves an average of 4.1% BD-rate reduction for the luma component under the All Intra configuration.

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“…It is divided into check procedure and comparison procedure. It initially checks for the rate distortion cost of the parent CTU [12] and the total cost of splitting it till end. Once this procedure is done comparison is done.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Deep Depth Decision Algorithm for Complexity Reduction in High Efficiency Video Coding (HEVC)

Joy¹,

Kounte²,

Sujatha³

2022

IJACSA

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High efficiency video (HEVC) coding made its mark as a codec which compress with low bit rate than its preceding codec that is H.264, but the factor that stop HEVC from many applications is its complex encoding procedure. The rate distortion optimisation (RDO) cost calculation in HEVC consume complex calculations. In this paper, we propose a method to cross out the issue of complex calculations by replacing the traditional inter-prediction procedure of brute force search for RDO by a deep convolutional neural network to predict and perform this process. In the first step, the modelling of the deep depth decision algorithm is done with optimum specifications using convolutional neural network (CNN). In the next step, the model is designed and trained with dataset and validated. The trained model is tested by pipelining it to the original HEVC encoder to check its performance. We also evaluate the efficiency of the model by comparing the average time of encoding for various resolution video input. The testing is done with mutually independent input to maintain the accuracy of the system. The system shows a substantial saving in encoding time that proves the complexity reduction in HEVC.

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Convolutional Neural Network-Based Arithmetic Coding for HEVC Intra-Predicted Residues

Cited by 24 publications

References 18 publications

Improving bit error-rate based on adaptive Bose-Chaudhuri Hocquenghem concatenated with convolutional codes

Improving bit error-rate based on adaptive Bose-Chaudhuri Hocquenghem concatenated with convolutional codes

Current Frame Priors Assisted Neural Network for Intra Prediction

Design and Implementation of Deep Depth Decision Algorithm for Complexity Reduction in High Efficiency Video Coding (HEVC)

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