Fast Intraprediction Algorithm for HEVC Based on Machine Learning Classification Technique

Sulochana, V.; Shanthini, B.; Harinath, K.

doi:10.1109/icdcece53908.2022.9793224

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…To reduce computational complexity, all CUs belonging to the significant group are replaced with RDs, thus minimizing the Rate dcistortion optimized (RDO) computation process. Literature [14] proposed to use a 5-stage support vector machine to accelerate the intra-frame mode decision process. Literature [15] uses a random forest classifier to determine the most likely split mode for each coding block and introduces a risk interval for classifier decision to reduce the possible coding loss caused by misclassification and achieves a balance between coding hairiness reduction and coding loss by changing the risk interval size.…”

Section: Background and Related Workmentioning

confidence: 99%

Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information

2023

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In order to satisfy the growing need for high-quality video, VVC comes with more efficient coding performance. According to statistical analysis, the level of coding complexity in VVC is tenfold greater compared to that of HEVC, so it is our main goal to study that what methods can be employed to decrease the time complexity of VVC. CU split in intra-frame modes requires the split mode decision by RD loss calculation, and the process of coding makes it to calculate RD calculation for all possible mode combinations, which is an important area that brings complexity to video coding, so in order to achieve our goal. Initially, we introduce an optimal depth prediction algorithm for Coding Units (CUs) by leveraging temporal combination. This algorithm collects depth information of CUs to predict the coding depth of CU blocks. Additionally, we suggest a decision tree-based method for CU split mode decision. With this method, we can make a decision on the CU split mode within the obtained split depth, reducing the time complexity of coding. This decision is based on the predictions from the first algorithm. The results demonstrate that our algorithm achieves superior performance over state-of-the-art methods in terms of computational complexity and compression quality. Compared to the VVC reference software (VTM), our method saves an average of 53.92% in coding time and improves the BDBR by 1.74%. These findings suggest that our method is highly effective in improving both computational efficiency and compression quality.

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Section: Background and Related Workmentioning

confidence: 99%

Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information

2023

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A Hardware-Friendlyand High-Efficiency H.265/HEVC Encoder for Visual Sensor Networks

Huang

Chen

2023

Sensors

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Visual sensor networks (VSNs) have numerous applications in fields such as wildlife observation, object recognition, and smart homes. However, visual sensors generate vastly more data than scalar sensors. Storing and transmitting these data is challenging. High-efficiency video coding (HEVC/H.265) is a widely used video compression standard. Compare to H.264/AVC, HEVC reduces approximately 50% of the bit rate at the same video quality, which can compress the visual data with a high compression ratio but results in high computational complexity. In this study, we propose a hardware-friendly and high-efficiency H.265/HEVC accelerating algorithm to overcome this complexity for visual sensor networks. The proposed method leverages texture direction and complexity to skip redundant processing in CU partition and accelerate intra prediction for intra-frame encoding. Experimental results revealed that the proposed method could reduce encoding time by 45.33% and increase the Bjontegaard delta bit rate (BDBR) by only 1.07% as compared to HM16.22 under all-intra configuration. Moreover, the proposed method reduced the encoding time for six visual sensor video sequences by 53.72%. These results confirm that the proposed method achieves high efficiency and a favorable balance between the BDBR and encoding time reduction.

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Fast Intraprediction Algorithm for HEVC Based on Machine Learning Classification Technique

Cited by 2 publications

References 12 publications

Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information

Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information

A Hardware-Friendlyand High-Efficiency H.265/HEVC Encoder for Visual Sensor Networks

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