RGB Medical Video Compression Using Geometric Wavelet

The video compression is used in a wide of applications from medical domain especially in telemedicine. Compared to the classical transforms, wavelet transform has significantly better performance in horizontal, vertical and diagonal directions. Therefore, this transform introduces high discontinuities in complex geometrics. However, to detect complex geometrics is one key challenge for the high efficient compression. In order to capture anisotropic regularity along various curves a new efficient and precise transform termed by bandelet basis, based on DWT, quadtree decomposition and optical flow is proposed in this paper. To encode significant coefficients we use efficient coder SPIHT. The experimental results show that the proposed algorithm DBT-SPIHT for low bit rate (0.3Mbps) is able to reduce up to 37.19% and 28.20% of the complex geometrics detection compared to the DWT-SPIHT and DCuT-SPIHT algorithm

Evaluation of the Medical Image Compression using Wavelet Packet Transform and SPIHT Coding

Wavelet transforms and wavelet packets are widely imposed in the analysis and resolution of problems related to science and technical engineering. Decomposition wavelet packet allows several frequency bands according to various levels of resolutions. We apply this transform (PWT) coupled with the SPIHT coder to reduce the limitations of conventional wavelet filter bank. The results obtained using the applied algorithm, are very satisfactory and encouraging compared to many of the best coders cited in the literature and show a visual and numerical superiority over conventional methods. These the promising results are confirmed by visual evaluation parameters (PSNR, MSSIM and VIF)

Improving quality of medical image compression using biorthogonal CDF wavelet based on lifting scheme and SPIHT coding

As the coming era is that of digitized medical information, an important challenge to deal with is the storage and transmission requirements of enormous data, including medical images. Compression is one of the indispensable techniques to solve this problem. In this work, we propose an algorithm for medical image compression based on a biorthogonal wavelet transform CDF 9/7 coupled with SPIHT coding algorithm, of which we applied the lifting structure to improve the drawbacks of wavelet transform. In order to enhance the compression by our algorithm, we have compared the results obtained with wavelet based filters bank. Experimental results show that the proposed algorithm is superior to traditional methods in both lossy and lossless compression for all tested images. Our algorithm provides very important PSNR and MSSIM values for MRI images

Image Vector Quantization codec indexes filtering

Vector Quantisation (VQ) is an efficient coding algorithm that has been widely used in the field of video and image coding, due to its fast decoding efficiency. However, the indexes of VQ are sometimes lost because of signal interference during the transmission. In this paper, we propose an efficient estimation method to conceal and recover the lost indexes on the decoder side, to avoid re-transmitting the whole image again. If the image or video has the limitation of a period of validity, re-transmitting the data wastes the resources of time and network bandwidth. Therefore, using the originally received correct data to estimate and recover the lost data is efficient in time-constrained situations, such as network conferencing or mobile transmissions. In nature images, the pixels are correlated with their neighbours and VQ partitions the image into sub-blocks and quantises them to the indexes that are transmitted; the correlation between adjacent indexes is very strong. There are two parts of the proposed method. The first is pre-processing and the second is an estimation process. In pre-processing, we modify the order of codevectors in the VQ codebook to increase the correlation among the neighbouring vectors. We then use a special filtering method in the estimation process. Using conventional VQ to compress the Lena image and transmit it without any loss of index can achieve a PSNR of 30.429 dB on the decoder. The simulation results demonstrate that our method can estimate the indexes to achieve PSNR values of 29.084 and 28.327 dB when the loss rate is 0.5% and 1%, respectively

Towards a new standard in medical video compression

International audienceThe objective of this article is to present a new video compression scheme with several potentialities in medical and industrial field. The proposed method will involve the process of transform, scaling and quantization, and it is based on bandelet transform coupled with the set partitioning in hierarchical trees (SPIHT) coding. The effectiveness of this technique results in combining two advantages. First it takes advantage of the anisotropic regularities of frames; second, it exploits the dependencies between the geometric transformed coefficients using SPIHT encoder to encode significant coefficients, while reducing redundancy, which, in turn, results in decreasing the volume of data without altering the pertinent information. The authors examined three scenarios, according to the above critical points of the visual quality of decompressed video. The first scenario concerns the choice of compression filter. The second identifies appropriate transform type, and the third validates the proposed algorithm with respect to classical algorithms. The performances of the proposed algorithm are evaluated using a set of objective video quality assessment metrics, including PSNR (peak signal-to-noise ratio), MSSIM (mean structural similarity) and VIF (visual information fidelity). The experimental results illustrate clearly the superiority of our algorithm with respect to Wavelet-SPIHT, MPEG-4, and H.264, H.265/MPEG-HEVC (video coding standards) when applied to a set of medical image tests

Improved Facial Expression Recognition Based on DWT Feature for Deep CNN

Facial expression recognition (FER) has become one of the most important fields of research in pattern recognition. In this paper, we propose a method for the identification of facial expressions of people through their emotions. Being robust against illumination changes, this method combines four steps: Viola–Jones face detection algorithm, facial image enhancement using contrast limited adaptive histogram equalization (CLAHE) algorithm, the discrete wavelet transform (DWT), and deep convolutional neural network (CNN). We have used Viola–Jones to locate the face and facial parts; the facial image is enhanced using CLAHE; then facial features extraction is done using DWT; and finally, the extracted features are used directly to train the CNN network, for the purpose of classifying the facial expressions. Our experimental work was performed on the CK+ database and JAFFE face database. The results obtained using this network were 96.46% and 98.43%, respectively

Investigation of the performance of modified TCM scheme for the protection of SPIHT-based compressed images over fading channel

International audienceIn this paper, a new variant of trellis-coded modulation with Ungerboeck-Gray mapping 'TCM-UGM' is presented for spectral efficiency of 2 bit/s/Hz. The performance of this encoding scheme is investigated over Rayleigh fading channel. The simulation result, using 16-state TCM-UGM encoder, shows clearly that the proposed scheme outperforms the performance of the 32-state TCM by 2.8 dB at BER=10-5. Both compression and transmission errors may degrade the quality of images. To illustrate the effectiveness of the proposed system for transmission the compressed image with the CDF9/7 wavelet and SPIHT coding, a comparison between the TCM and the new variant the TCM-UGM is presented. Simulation results on several types of compressed image were presented. For this study, four Image Quality Metrics (IQMs) were employed in the image quality assessment after the transmission. Results show that the new variant of TCM-UGM reduces transmission errors and better protects the compressed image during transmission