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Publication Type : Journal Article
Publisher : Elsevier BV
Source : Journal of Visual Communication and Image Representation
Url : https://doi.org/10.1016/j.jvcir.2022.103578
Keywords : Distributed video coding, Chroma prediction, Convolutional neural networks, Video compression, Wireless capsule endoscopy
Campus : Bengaluru
School : School of Engineering
Department : Electronics and Communication
Year : 2022
Abstract : Compression of captured video frames is crucial for saving the power in wireless capsule endoscopy (WCE). A low complexity encoder is desired to limit the power consumption required for compressing the WCE video. Distributed video coding (DVC) technique is best suitable for designing a low complexity encoder. In this technique, frames captured in RGB colour space are converted into YCbCr colour space. Both Y and CbCr representing luma and chroma components of the Wyner–Ziv (WZ) frames are processed and encoded in existing DVC techniques proposed for WCE video compression. In the WCE video, consecutive frames exhibit more similarity in texture and colour properties. The proposed work uses these properties to present a method for processing and encoding only the luma component of a WZ frame. The chroma components of the WZ frame are predicted by an encoder–decoder based deep chroma prediction model at the decoder by matching luma and texture information of the keyframe and WZ frame. The proposed method reduces the computations required for encoding and transmitting of WZ chroma component. The results show that the proposed DVC with a deep chroma prediction model performs better when compared to motion JPEG and existing DVC systems for WCE at the reduced encoder complexity.
Cite this Research Publication : Sushma B., Aparna P., Deep chroma prediction of Wyner–Ziv frames in distributed video coding of wireless capsule endoscopy video, Journal of Visual Communication and Image Representation, Elsevier BV, 2022, https://doi.org/10.1016/j.jvcir.2022.103578