Abstract
The area of Image processing has emerged with different coding approaches, and applications which are ranging from fundamental image compression model to high quality applications. The advancement of image processing, has given the advantage of automation in various image coding applications, among which medical image processing is one of the prime area. Medical diagnosis has always remained a time taking and sensitive approach for accurate medical treatment. Towards improving these issues, automation systems have been developed. In the process of automation, the images are processed and passed to a remote processing unit for processing and decision making. It is observed that, images are coded for compression to minimize the processing and computational overhead. However, the issue of compressing data over accuracy always remains a challenge. Thus, for an optimization in image compression, there is a need for compression through the reduction of non-relevant coefficients in medical images. The proposed image compression model helped in developing a coding technique to attain accurate compression by retaining image precision with lower computational overhead in clinical image coding. Towards making the image compression more efficient, this research work introduces an approach of image compression based on learning coding. This research achieves superior results in terms of Compression rate, Encoding time, Decoding time, Total processing time and Peak signal-to-noise ratio (PSNR).
Highlights
Image processing and its related applications has ascended in different levels of coding approach which are stretching from rudimentary image compression model to astronomical data processing and clinical image processing, considered as high end applications
The evaluation and performance of developed Neural Networks (NN) coding is measured through parameters such Compression Rate (CR), Encoding Time (ET), Decoding Time (DT), Total Time (TT) for processing and Peak Signal to Noise Ratio (PSNR) and compared with the existing JPEG coding
The coding is developed for the image pixel selection, where the learning approach of neural network is used for the selection of significant coefficients
Summary
Image processing and its related applications has ascended in different levels of coding approach which are stretching from rudimentary image compression model to astronomical data processing and clinical image processing, considered as high end applications. For example in telemedicine, sending and receiving images by overcoming the bandwidth limitation is a major problem faced by the hospitals nowadays. In this situation, an engineer’s main aim is to develop new methods using which the transmission of multiple images with lower bitrate can be made easy. The images are of huge features, coding without the loss of information into lower bit rates may intern results to the degradation in the quality of image under retrieval. Along with this, encoding in the noise environment becomes too much complex and results in the heavier degradation in the quality of image. Several approaches were proposed in the past for encoding and compression of images, but none of them found to be efficient under specific environments
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