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Mr Gosala Kulupana

Research Fellow

Academic and research departments

Department of Electrical and Electronic Engineering.


My publications


Kulupana Gosala, Talagala Dumidu S, Arachchi Hemantha Kodikara, Fernando Anil End User Video Quality Prediction and Coding Parameters Selection at the Encoder for Robust HEVC Video Transmission,IEEE Transactions on Circuits and Systems for Video Technology IEEE
Abstract?Along with the rapid increase in the availability for high quality video formats such as HD (High Definition), UHD (Ultra HD) and HDR (High Dynamic Range), a huge demand for data rates during their transmission has become inevitable. Consequently, the role of video compression techniques has become crucially important in the process of mitigating the data rate requirements. Even though the latest video codec HEVC (High Efficiency Video Coding) has succeeded in significantly reducing the data rate compared to its immediate predecessor H.264/AVC (Advanced Video Coding), the HEVC coded videos in the meantime have become even more vulnerable to network impairments. Therefore, it is equally important to assess the consumers? perceived quality degradation prior to transmitting HEVC coded videos over an error prone network, and to include error resilient features so as to minimize the adverse effects those impairments. To this end, this paper proposes a probabilistic model which accurately predicts the overall distortion of the decoded video at the encoder followed by an accurate QP-» relationship which can be used in the RDO (Rate Distortion Optimization) process. During the derivation process of the probabilistic model, the impacts from the motion vectors, the pixels in the reference frames and the clipping operations are accounted and consequently the model is capable of minimizing the prediction error as low as 3.11% whereas the state-of-theart methods can?t reach below 20.08% under identical conditions. Furthermore, the enhanced RDO process has resulted in 21.41%- 43.59% improvement in the BD-rate compared to the state-ofthe-art error resilient algorithms.
Erabadda Buddhiprabha, Mallikarachchi Thanuja, Kulupana Gosala, Fernando Anil (2018) Machine Learning Approaches for Intra-Prediction in HEVC,Proceedings of the 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE)pp. 206-209 Institute of Electrical and Electronics Engineers (IEEE)
The use of machine learning techniques for encoding complexity reduction in recent video coding standards such as High Efficiency Video Coding (HEVC) has received prominent attention in the recent past. Yet, the dynamically changing nature of the video contents makes it evermore challenging to use rigid traditional inference models for predicting the encoding decisions for a given content. In this context, this paper investigates the resulting implications on the coding efficiency and the encoding complexity, when using offline trained and online trained machine-learning models for coding unit size selection in the HEVC intra-prediction. The experimental results demonstrate that the ground truth encoding statistics of the content being encoded, is crucial to the efficient encoding decision prediction when using machine learning based prediction models.
Kulupana Gosala, Talagala Dumidu, Fernando Anil, Arachchi Hemantha Kodikara (2018) Bit allocation and encoding parameter selection for rate-controlled error resilient HEVC video encoding,Proceedings of the 2018 IEEE International Conference on Consumer Electronics (ICCE)pp. 1-4 Institute of Electrical and Electronics Engineers (IEEE)
Even though the latest video compression techniques such as High Efficiency Video coding (HEVC) have succeeded in significantly alleviating the bandwidth consumption during high resolution video transmission, they have become severely susceptible to transmission errors. Overcoming the resulting temporal impact of the transmission errors on the decoded video requires efficient error resilient schemes that can introduce robustness features to the coded video in order to mitigate the negative impact on the viewer. To this end, this paper proposes a rate-controlled error resilient bit allocation scheme, together with an encoding parameter selection process, to adaptively determine the most robust video coding parameters and the decoder error concealment operations during the encoding itself. Consequently, the proposed method has demonstrated 0.48dB-0.62dB PSNR gain over the state-of-the art methods at the same bit rate.
Erabadda Buddhiprabha, Mallikarachchi Thanuja, Kulupana Gosala, Fernando Anil (2019) Content Adaptive Fast CU Size Selection for HEVC Intra-Prediction,Proceedings of the 2019 IEEE International Conference on Consumer Electronics (ICCE)pp. 1-2 Institute of Electrical and Electronics Engineers (IEEE)
This paper proposes a content adaptive fast CU size selection algorithm for HEVC intra-prediction using weighted support vector machines. The proposed algorithm demonstrates an average encoding time reduction of 52.38% with 1.19% average BDBR increase compared to HM16.1 reference encoder.