Faculty Publications
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Item Texture Classification based Efficient Image Compression Algorithm for Wireless Capsule Endoscopy(Institute of Electrical and Electronics Engineers Inc., 2019) Sushma, B.; Aparna., P.This paper presents a novel method for classification of blocks into smooth and edge blocks in transform domain and a compression scheme for Wireless Capsule Endoscopy (WCE) with block classifier. WCE involves capturing, transmission and processing of gastrointestinal images. Power consumption is a critical issue in WCE, as it uses a button battery driven capsule endoscope to capture and transmit images. The captured image needs to be compressed to save the transmission power and low complexity compressor should be used to avoid more power consumption from the compressor itself. JPEG based compression techniques which consists Discrete Cosine Transform(DCT), quantizer and entropy encoder provides the best compression performance with less complexity compared to other various techniques. Pixel distribution in smooth blocks is uniform and energy is compacted only into low frequency bands in spectral domain. Because high frequency bands are almost having zero energy, only low frequency bands are quantized and entropy coded which saves power in processing high bands. Most of the endoscopic image has smooth region, this method is more suitable to WCE. Proposed algorithm improves compression rate by 9% without sacrificing quality compared to JPEG based compression algorithm. © 2019 IEEE.Item Deep chroma prediction of Wyner–Ziv frames in distributed video coding of wireless capsule endoscopy video(Academic Press Inc., 2022) Sushma, B.; Aparna., P.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. © 2022 Elsevier Inc.