Browsing by Author "Tahir, Muhammad"
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- Fast video encoding based on random forestsPublication . Tahir, Muhammad; Taj, Imtiaz A.; Assuncao, Pedro A. A.; Asif, MuhammadMachine learning approaches have been increasingly used to reduce the high computational complexity of high-efficiency video coding (HEVC), as this is a major limiting factor for real-time implementations, due to the decision process required to find optimal coding modes and partition sizes for the quad-tree data structures defined by the standard. This paper proposes a systematic approach to reduce the computational complexity of HEVC based on an ensemble of online and offline Random Forests classifiers. A reduced set of features for training the Random Forests classifier is proposed, based on the rankings obtained from information gain and a wrapper-based approach. The best model parameters are also obtained through a consistent and generalizable method. The proposed Random Forests classifier is used to model the coding unit and transform unit-splitting decision and the SKIP-mode prediction, as binary classification problems, taking advantage from the combination of online and offline approaches, which adapts better to the dynamic characteristics of video content. Experimental results show that, on average, the proposed approach reduces the computational complexity of HEVC by 62.64% for the random access (RA) profile and 54.57% for the low-delay (LD) main profile, with an increase in BD-Rate of 2.58% for RA and 2.97% for LD, respectively. These results outperform the previous works also using ensemble classifiers for the same purpose.
- Low complexity high efficiency coding of light fields using ensemble classifiersPublication . Tahir, Muhammad; Taj, Imtiaz A.; Assuncao, Pedro A.; Asif, MuhammadLight field images can be efficiently compressed using standard video codecs, such as the High Efficiency Video Coding (HEVC). However, the huge amount of data combined with the high computational complexity of HEVC, poses limitations on high-speed light field capturing and storage. This paper presents a contribution for low complexity encoding of light fields, in different formats using HEVC, based on a Random Forests ensemble classifier. Optimal features for training the classifier are found through a score fusion based approach. Using the HEVC still image profile, the proposed method gives speed-up of 56.23% for sub-aperture images. For pseudo video format, the proposed method outperforms others available in the literature, yielding an average speed-up of 62.18%, 56.54% and 44.73% for Random Access, Low-delay Main and All-Intra profiles respectively, with negligible decrease in RD performance. These are novel results in fast coding of light fields, which are useful for further research and benchmarking.