LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Novel chroma subsampling patterns for wireless capsule endoscopy compression

Photo from wikipedia

The recently established wireless capsule endoscopy promises itself to be a great advancement in the field of medical diagnosis. The amount of data generated due to a single inspection of… Click to show full abstract

The recently established wireless capsule endoscopy promises itself to be a great advancement in the field of medical diagnosis. The amount of data generated due to a single inspection of gastrointestinal tract is huge, and the transmission demands more cost and power. Due to the inherent nature of the capsule, a low complexity and low-power design are highly recommended with the ability to reduce the data. This paper explains the design of a compression module which satisfies the above requirements. The compression algorithm is developed around some features of the endoscopic images with its suitability for hardware implementation. The preprocessing operations followed by two-stage encoder using differential coder and Golomb–Rice coder are implemented on the experimental dataset. The core module in the preprocessing stage is the chroma subsampling which can considerably reduce the data to be transmitted. Several patterns for subsampling have been analyzed in this paper to evaluate the performance of the proposed system. With the help of a suitably designed up-sampler at the receiver, a near-lossless compression with good reconstructed image quality can be achieved. The 22:1:2 pattern yields better results and could bring about an average peak signal-to-noise ratio of 37 dB with compression rate of 70%. The results show that the proposed chroma subsampling patterns performs better than the competing methods towards wireless capsule endoscopy compression.

Keywords: compression; capsule; wireless capsule; chroma subsampling; capsule endoscopy

Journal Title: Neural Computing and Applications
Year Published: 2019

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.