Pablo Manrique Ramírez

Adaptive wavelet transform algorithm for image compression applications

A new algorithm of locally adaptive wavelet transform is presented. The algorithm implements the integer-to-integer lifting scheme. It performs an adaptation of the wavelet function at the prediction stage to the local image data activity. The proposed algorithm is based on the generalized framework for the lifting scheme that permits to obtain easily different wavelet coefficients in the case of the (N~,N) lifting. It is proposed to perform the hard switching between (2, 4) and (4, 4) lifting filter outputs according to an estimate of the local data activity. When the data activity is high, i.e., in the vicinity of edges, the (4, 4) lifting is performed. Otherwise, in the plain areas, the (2,4) decomposition coefficients are calculated. The calculations are rather simples that permit the implementation of the designed algorithm in fixed point DSP processors. The proposed adaptive transform possesses the perfect restoration of the processed data and possesses good energy compactation. The designed algorithm was tested on different images. The proposed adaptive transform algorithm can be used for image/signal lossless compression.

Image Filter Based on Block Matching, Discrete Cosine Transform and Principal Component Analysis

An algorithm for filtering the images contaminated by additive white Gaussian noise is proposed. The algorithm uses the groups of Hadamard transformed patches of discrete cosine coefficients to reject noisy components according to Wiener filtering approach. The groups of patches are found by the proposed block similarity search algorithm of reduced complexity performed on block patches in transform domain. When the noise variance is small, the proposed filter uses an additional stage based on principal component analysis; otherwise the experimental Wiener filtering is performed. The obtained filtering results are compared to the state of the art filters in terms of peak signal-to-noise ratio and structure similarity index. It is shown that the proposed algorithm is competitive in terms of signal to noise ratio and almost in all cases is superior to the state of the art filters in terms of structure similarity.

Environmental pattern recognition for assessment of air quality data with the Gamma classifier

Nowadays efficient methods for air quality assessment are needed in order to detect negative problems in human health. A new computational model is developed in order to evaluate toxic compounds in air of urban areas that can be harmful in sensitive people, affecting their normal activities. Using the Gamma classifier (Γ), environmental variables are assessed determining their negative impact in air quality based on their toxicity limits, the average of the frequency and the deviations of toxic tests. A fuzzy inference system uses the environmental classifications providing an air quality index, which describes the pollution levels in five stages: excellent, good, regular, bad and danger respectively.

Image Noise Filter Based on DCT and Fast Clustering

An algorithm for filtering images contaminated by additive white Gaussian noise in discrete cosine transform domain is proposed. The algorithm uses a clustering stage to obtain mean power spectrum of each cluster. The groups of clusters are found by the proposed fast algorithm based on 2D histograms and watershed transform. In addition to the mean spectrum of each cluster, the local groups of similar patches are found to obtain the local spectrum, and therefore, derive the local Wiener filter frequency response better and perform the collaborative filtering over the groups of patches. The obtained filtering results are compared to the state-of-the-art filters in terms of peak signal-to-noise ratio and structural similarity index. It is shown that the proposed algorithm is competitive in terms of signal-to-noise ratio and in almost all cases is superior to the state-of-the art filters in terms of structural similarity.

Wavelet Filter Adjusting for Image Lossless Compression Using Pattern Recognition

A method for image lossless compression using lifting scheme wavelet transform is presented. The proposed method adjusts wavelet filter coefficients analyzing signal spectral characteristics to obtain a higher compression ratio in comparison to the standard CDF(2,2) and CDF(4,4) filters. The proposal is based on spectral pattern recognition with 1-NN classifier. Spectral patterns of a small fixed length are formed for the entire image permitting thus the global optimization of the filter coefficients, equal for all decompositions. The proposed method was applied to a set of test images obtaining better results in entropy values in comparison to the standard wavelet lifting filters.

Turbo codification techniques for error control in a communication channel

An implementation of the turbo coding technique for data error detection and correction in data transmission is presented. The turbo coding technique is known to be efficient in data transmission adding redundant parity that provides a high error correction capacity decreasing the number of erroneous bits for low signal to noise ratios increasing the number of iterations. The turbo encoder and turbo decoder were implemented in a FPGA development system. The design is oriented to reach a transmission speed near to the theoretical Shannons capacity of the communication channel and minimum possible energy consumption using only the FPGA resources without external memories.

Data dependent wavelet filtering for lossless image compression

A data dependent wavelet transform based on the modified lifting scheme is presented. The algorithm is based on the wavelet filters derived from a generalized lifting scheme. The proposed framework for the lifting scheme permits to obtain easily different wavelet FIR filter coefficients in the case of the (~N, N) lifting. To improve the performance of the lifting filters the presented technique additionally realizes IIR filtering by means of the feedback to the already calculated wavelet coefficients. The perfect image restoration in this case is obtained employing the particular features of the lifting scheme. Changing wavelet FIR filter order and/or FIR and IIR coefficients, one can obtain the filter frequency response that match better to the image data than the standard lifting filters, resulting in higher data compression rate. The designed algorithm was tested on different images. The obtained simulation results show that the proposed method performs better in data compression for various images in comparison to the standard technique resulting in significant savings in compressed data length.

Adaptive wavelet transform algorithm for lossy image compression

A new algorithm of locally adaptive wavelet transform based on the modified lifting scheme is presented. It performs an adaptation of the wavelet high-pass filter at the prediction stage to the local image data activity. The proposed algorithm uses the generalized framework for the lifting scheme that permits to obtain easily different wavelet filter coefficients in the case of the (~N, N) lifting. Changing wavelet filter order and different control parameters, one can obtain the desired filter frequency response. It is proposed to perform the hard switching between different wavelet lifting filter outputs according to the local data activity estimate. The proposed adaptive transform possesses a good energy compaction. The designed algorithm was tested on different images. The obtained simulation results show that the visual and quantitative quality of the restored images is high. The distortions are less in the vicinity of high spatial activity details comparing to the non-adaptive transform, which introduces ringing artifacts. The designed algorithm can be used for lossy image compression and in the noise suppression applications.

Impulse rejecting filter for efficient noise removal and fine detail preservation

A novel filtering algorithm applicable to image processing is presented. It was designed using rank-ordered mean (ROM) estimator to remove an outlier and robust local data activity estimators to detect the outliers. The proposed filter effectively remove impulse noise and preserve edge and fine details. The filter possesses good visual quality of the processed simulated images and good quantitative quality in comparison to the standard median filter. Recommendations to obtain best processing results by proper selection of the filter parameters are given. The designed filter is suitable for impulse noise removal in any image processing applications. One can use it at the first stage of image enhancement followed by any detail-preserving techniques such as the Sigma filter at the second stage.