Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction

Erabadda, Buddhiprabha; Mallikarachchi, Thanuja; Hewage, Chaminda; Fernando, Anil

doi:10.3390/fi11080175

Cited by 3 publications

(3 citation statements)

References 40 publications

(73 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Buddhiprabha Erabadda, Thanuja Mallikarachchi, Chaminda Hewage and Anil Fernando are the authors of the second paper [7], which explores the resource requirements for the real-time operation of High Efficiency video Coding (HEVC), which has become a contributing factor towards the QoE of the end users of emerging multimedia and future internet applications. In this context, this study proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction.…”

Section: Contributionsmentioning

confidence: 99%

Multimedia Quality of Experience (QoE): Current Status and Future Direction

Hewage

Ekmekcioǧlu

2020

Future Internet

Self Cite

View full text Add to dashboard Cite

Quality of Experience (QoE) is becoming an important factor of User-Centred Design (UCD). The deployment of pure technical measures such as Quality of Service (QoS) parameters to assess the quality of multimedia applications is phasing out due to the failure of those methods to quantify true user satisfaction. Though significant research results and several deployments have occurred and been realized over the last few years, focusing on QoE-based multimedia technologies, several issues both of theoretical and practical importance remain open. Accordingly, the papers of this Special Issue are significant contribution samples within the general ecosystem highlighted above, ranging from QoE in the capture, processing and consumption of next-generation multimedia applications. In particular, a total of five excellent articles have been accepted, following a rigorous review process, which address many of the aforementioned challenges and beyond.

show abstract

Section: Contributionsmentioning

confidence: 99%

Multimedia Quality of Experience (QoE): Current Status and Future Direction

Hewage

Ekmekcioǧlu

2020

Future Internet

Self Cite

View full text Add to dashboard Cite

show abstract

“…Both SVMs and CNNs are aimed at reducing the HEVC computation complexity to the maximum extent of reducing the quality loss of video content. However, due to the inflexibility of the decision boundary in HEVC video coding, the SVM methods [17][18][19] cannot control the complexity during coding. Some scholars extend single-classification SVM to multi-classifications SVM to partition CTU.…”

Section: Introductionmentioning

confidence: 99%

A Method to Reduce the Intra-Frame Prediction Complexity of HEVC Based on D-CNN

Wang

et al. 2023

Electronics

View full text Add to dashboard Cite

Among a series of video coding standards jointly developed by ITU-T, VCEG, and MPEG, high-efficiency video coding (HEVC) is one of the most widely used video coding standards today. Therefore, it is still necessary to further reduce the coding complexity of HEVC. In the HEVC standard, a flexible partitioning procedure entitled “quad-tree partition” is proposed to significantly improve the coding efficiency, which, however, leads to high coding complexity. To reduce the coding complexity of the intra-frame prediction, this paper proposes a scheme based on a densely connected convolution neural network (D-CNN) to predict the partition of coding units (CUs). Firstly, a densely connected block was designed to improve the efficiency of the CU partition by fully extracting the pixel features of CTU. Then, efficient channel attention (ECA) and adaptive convolution kernel size were applied to a fast CU partition for the first time to capture the information of the D-CNN convolution channels. Finally, a threshold optimization strategy was formulated to select the best threshold for each depth to further balance the computation complexity of video coding and the performance of RD. The experimental results show that the proposed method reduces the encoding time of HEVC by 60.14%, with a negligible reduction in RD performance, which is better than the existing fast partitioning methods.

show abstract

“…(The term decoding-complexity in this manuscript refers to the number of CPU instructions consumed by the processor to decode an encoded bit stream.) This will become more pronounced with time, especially due to the complexity of modern standards such as High Efficiency Video Coding (HEVC) and the upcoming Versatile Video Coding (VVC), and the increasing demand for high definition (HD) content on hand-held devices [3,4]. As a solution, simultaneous control of both bit rate and decoding-complexity of an encoded video sequence could potentially address both these constraints.…”

Section: Introductionmentioning

confidence: 99%

A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC

et al. 2020

Self Cite

View full text Add to dashboard Cite

Video playback on mobile consumer electronic (CE) devices is plagued by fluctuations in the network bandwidth and by limitations in processing and energy availability at the individual devices. Seen as a potential solution, the state-of-the-art adaptive streaming mechanisms address the first aspect, yet the efficient control of the decoding-complexity and the energy use when decoding the video remain unaddressed. The quality of experience (QoE) of the end-users’ experiences, however, depends on the capability to adapt the bit streams to both these constraints (i.e., network bandwidth and device’s energy availability). As a solution, this paper proposes an encoding framework that is capable of generating video bit streams with arbitrary bit rates and decoding-complexity levels using a decoding-complexity–rate–distortion model. The proposed algorithm allocates rate and decoding-complexity levels across frames and coding tree units (CTUs) and adaptively derives the CTU-level coding parameters to achieve their imposed targets with minimal distortion. The experimental results reveal that the proposed algorithm can achieve the target bit rate and the decoding-complexity with 0.4% and 1.78% average errors, respectively, for multiple bit rate and decoding-complexity levels. The proposed algorithm also demonstrates a stable frame-wise rate and decoding-complexity control capability when achieving a decoding-complexity reduction of 10.11 (%/dB). The resultant decoding-complexity reduction translates into an overall energy-consumption reduction of up to 10.52 (%/dB) for a 1 dB peak signal-to-noise ratio (PSNR) quality loss compared to the HM 16.0 encoded bit streams.

show abstract

Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction

Cited by 3 publications

References 40 publications

Multimedia Quality of Experience (QoE): Current Status and Future Direction

Multimedia Quality of Experience (QoE): Current Status and Future Direction

A Method to Reduce the Intra-Frame Prediction Complexity of HEVC Based on D-CNN

A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC

Contact Info

Product

Resources

About