COMPACT REPRESENTATION AND PANORAMIC REPRESENTATION FOR CAPSULE ENDOSCOPE IMAGES

2009 ◽  
Vol 06 (04) ◽  
pp. 257-268 ◽  
Author(s):  
CHAO HU ◽  
LI LIU ◽  
BO SUN ◽  
MAX Q.-H. MENG
Keyword(s):  
Gastrointestinal Tract ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Medical Instrument ◽  
Compact Representation ◽  
Capsule Endoscope ◽  
High Compression Ratio ◽  
Signal To Noise ◽  
Wavelet Compression ◽  
Endoscopic Images

A capsule endoscope robot is a miniature medical instrument for inspection of gastrointestinal tract. In this paper, we present image compact representation and preliminary panoramic representation methods for the capsule endoscope. First, the characteristics of the capsule endoscopic images are investigated and different coordinate representations of the circular image are discussed. Secondly, effective compact representation methods including special DPCM and wavelet compression techniques are applied to the endoscopic images to get high compression ratio and signal to noise ratio. Then, a preliminary approach to panoramic representation of endoscopic images is presented.

scholarly journals 3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1817
Author(s):  
Jiawen Xue ◽  
Li Yin ◽  
Zehua Lan ◽  
Mingzhu Long ◽  
Guolin Li ◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Blocking Artifacts ◽  
Compression Method ◽  
High Compression Ratio ◽  
Multiplication Operation ◽  
3D Data ◽  
Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

scholarly journals New Speech Compression Technique based on Filter Bank Design and Psychoacoustic Model

2019 ◽  
Vol 24 (4) ◽  
pp. 728-735
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Compression Ratio ◽  
Filter Bank ◽  
Signal To Noise Ratio ◽  
Speech Signals ◽  
Speech Compression ◽  
Signal To Noise ◽  
Compression Technique ◽  
Psychoacoustic Model ◽  
Perceptual Evaluation ◽  
Noise Ratio

In this paper, a new speech compression technique is proposed. This technique applies a Psychoacoustic Model and a general approach for Filter Bank Design using optimization. It is evaluated and compared with a compression technique using a MDCT (Modified Discrete Cosine Transform) Filter Bank of 32 Filters and a Psychoacoustic Model. This evaluation and comparison is performed by calculating bits before and after compression, PSNR (Peak Signal to Noise Ratio), NRMSE (Normalized Root Mean Square Error), SNR (Signal to Noise Ratio) and PESQ (Perceptual evaluation of speech quality) computations. The two techniques are tested and applied to a number of speech signals that are sampled at 8 kHz. The results obtained from this evaluation show that the proposed technique outperforms the second compression technique (based on a Psychoacoustic Model and MDCT filter Bank) in terms of Bits after compression and compression ratio. In fact, the proposed technique yields higher values for the compression ratio than the second compression technique. Moreover, the proposed compression technique presents reconstructed speech signals with acceptable perceptual qualities. This is justified by the values of SNR, PSNR and NRMSE and PESQ.

Optimization Before Biomedical Image Compression Using CLAHE and DCS

2018 ◽  
Vol 7 (3.27) ◽  
pp. 236
Author(s):  
Satyawati S. Magar ◽  
Bhavani Sridharan
Keyword(s):  
Medical Imaging ◽  
Image Compression ◽  
Image Enhancement ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Biomedical Image ◽  
Adaptive Histogram Equalization ◽  
Noise Ratio

In current years, improving the Compression Ratio (CR) in medical imaging is essential and becomes big challenge in the field of biomedical. In that direction we have done optimization before biomedical image compression. For the same we have used the image enhancement techniques. For the enhancement of an image we have used Contrast Limited Adaptive Histogram Equalization (CLAHE) and Decorrelation Stretch (DCS) algorithms. By optimizing an image before compression we have achieved better Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR) than existing methods of an image compression. Mainly results are compared with Oscillation Concept method of an image compression with and without optimization.  

Image Compression Technique for Low Bit Rate Transmission

2011 ◽  
Vol 1 (4) ◽  
pp. 1-18
Author(s):  
Shaimaa A. El-said ◽  
Khalid F. A. Hussein ◽  
Mohamed M. Fouad
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Bit Rate ◽  
High Compression Ratio ◽  
Compression Technique ◽  
Low Bit Rate ◽  
High Compression ◽  
Rate Transmission ◽  
Visual Degradation

A novel Adaptive Lossy Image Compression (ALIC) technique is proposed to achieve high compression ratio by reducing the number of source symbols through the application of an efficient technique. The proposed algorithm is based on processing the discrete cosine transform (DCT) of the image to extract the highest energy coefficients in addition to applying one of the novel quantization schemes proposed in the present work. This method is straightforward and simple. It does not need complicated calculation; therefore the hardware implementation is easy to attach. Experimental comparisons are carried out to compare the performance of the proposed technique with those of other standard techniques such as the JPEG. The experimental results show that the proposed compression technique achieves high compression ratio with higher peak signal to noise ratio than that of JPEG at low bit rate without the visual degradation that appears in case of JPEG.

scholarly journals The Arithmetic Coding and Hybrid Discrete Wavelet and Cosine Transform Approaches in Image Compression

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Nassir H. Salman ◽  
S. Rafea
Keyword(s):  
Magnetic Resonance Imaging ◽  
Wavelet Transform ◽  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Arithmetic Coding ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Resonance Imaging ◽  
Signal To Noise

Image compression is one of the data compression types applied to digital images in order to reduce their high cost for storage and/or transmission. Image compression algorithms may take the benefit of visual sensitivity and statistical properties of image data to deliver superior results in comparison with generic data compression schemes, which are used for other digital data. In the first approach, the input image is divided into blocks, each of which is 16 x 16, 32 x 32, or 64 x 64 pixels. The blocks are converted first into a string; then, encoded by using a lossless and dictionary-based algorithm known as arithmetic coding. The more occurrence of the pixels values is codded in few bits compare with pixel values of less occurrence through the sub intervals between the range 0 and 1. Finally, the stream of compressed tables is reassembled for decompressing (image restoration). The results showed a compression gain of 10-12% and less time consumption when applying this type of coding to each block rather than the entire image. To improve the compression ratio, the second approach was used based on the YCbCr colour model. In this regard, images were decomposed into four sub-bands (low-low, high-low, low-high, and high-high) by using the discrete wavelet transform compression algorithm. Then, the low-low sub-band was transmuted to frequency components (low and high) via discrete wavelet transform. Next, these components were quantized by using scalar quantization and then scanning in a zigzag way. The compression ratio result is 15.1 to 27.5 for magnetic resonance imaging with a different peak signal to noise ratio and mean square error; 25 to 43 for X-ray images; 32 to 46 for computed tomography scan images; and 19 to 36 for magnetic resonance imaging brain images. The second approach showed an improved compression scheme compared to the first approach considering compression ratio, peak signal to noise ratio, and mean square error.

Image Compression Technique for Low Bit Rate Transmission

2013 ◽  
pp. 1306-1322
Author(s):  
Shaimaa A. El-said ◽  
Khalid F. A. Hussein ◽  
Mohamed M. Fouad
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Bit Rate ◽  
High Compression Ratio ◽  
Compression Technique ◽  
Low Bit Rate ◽  
High Compression ◽  
Rate Transmission ◽  
Visual Degradation

A novel Adaptive Lossy Image Compression (ALIC) technique is proposed to achieve high compression ratio by reducing the number of source symbols through the application of an efficient technique. The proposed algorithm is based on processing the discrete cosine transform (DCT) of the image to extract the highest energy coefficients in addition to applying one of the novel quantization schemes proposed in the present work. This method is straightforward and simple. It does not need complicated calculation; therefore the hardware implementation is easy to attach. Experimental comparisons are carried out to compare the performance of the proposed technique with those of other standard techniques such as the JPEG. The experimental results show that the proposed compression technique achieves high compression ratio with higher peak signal to noise ratio than that of JPEG at low bit rate without the visual degradation that appears in case of JPEG.

Image Compression Technique for Low Bit Rate Transmission

2013 ◽  
pp. 211-229
Author(s):  
Shaimaa A. El-said ◽  
Khalid F. A. Hussein ◽  
Mohamed M. Fouad
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Bit Rate ◽  
High Compression Ratio ◽  
Compression Technique ◽  
Low Bit Rate ◽  
High Compression ◽  
Rate Transmission ◽  
Visual Degradation

A novel Adaptive Lossy Image Compression (ALIC) technique is proposed to achieve high compression ratio by reducing the number of source symbols through the application of an efficient technique. The proposed algorithm is based on processing the discrete cosine transform (DCT) of the image to extract the highest energy coefficients in addition to applying one of the novel quantization schemes proposed in the present work. This method is straightforward and simple. It does not need complicated calculation; therefore the hardware implementation is easy to attach. Experimental comparisons are carried out to compare the performance of the proposed technique with those of other standard techniques such as the JPEG. The experimental results show that the proposed compression technique achieves high compression ratio with higher peak signal to noise ratio than that of JPEG at low bit rate without the visual degradation that appears in case of JPEG.

scholarly journals Performance analysis of landsat5 remote sensing data compression technique used for land use and land cover mapping

2014 ◽  
Vol 3 (3) ◽  
pp. 308
Author(s):  
Kousalyadevi Rajamanickam ◽  
J. Suganthi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Land Cover Mapping ◽  
Signal To Noise ◽  
Error Matrix ◽  
Digital Number ◽  
Noise Ratio

Multispectral band remote sensing imagery is used for environmental monitoring and land use and land cover mapping purposes. This image contains huge volume of data. Instead of using the entire data for land use land cove mapping, the spatially compressed images can also be used for mapping purposes. In this paper discrete wavelet transform is selected for compressing the Landsat5 image and the quality has been analysed using the parameters compression ratio, peak signal to noise ratio and digital number values. Using the digital number values the spectral signature graph is drawn. Finally only one wavelet is selected for land use and land cover mapping based on minimum cumulative error of the digital number values. Then the selected wavelet compressed image is classified using supervised classification technique and accuracy assessment is made by constructing the error matrix. Finally the selected wavelet compressed image is used for land use and land cover mapping. Keywords: Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Digital Number (DN), Image Classification, Error Matrix, Spectral Signatures.

Sign in / Sign up

Export Citation Format

Share Document