Transparent Composite Model Research Articles

Quantization based wavelet transformation technique for digital image compression with removal of multiple artifacts and noises

A Morlet’s wavelet transformation based image compression and decompression (MWT-ICD) technique is proposed in order to enhance the performance of digital and gray scale image compression with higher compression ratio (CR) and to reduce the space complexity. The MWT-ICD technique initially performs preprocessing task to remove multiple artifacts and noises in digital and gray scale images with the application of generalized lapped orthogonal transforms and Wiener filter. This process results in improved quality of digital and gray scale images with higher PSNR for compression. Next, wavelet quantization transformation based image compression algorithm is developed in MWT-ICD Technique using Morlet’s wavelet transformation. Finally, quantized wavelet transformation based image decompression process is carried out in MWT-ICD technique with the objective of obtaining the reconstructed original image. The performance of MWT-ICD technique is measured in terms of CR, compression time (CT), and space complexity (SC), peak signal to noise ratio (PSNR) and compared with four existing methods. The experimental results show that the MWT-ICD technique is able to acquire higher CR and also reduced space complexity when compared to existing DCT-based image compression system based on Laplacian transparent composite model and multi-wavelet based compressed sensing technique

Fast Mode Selection for HEVC Intra-Frame Coding With Entropy Coding Refinement Based on a Transparent Composite Model

In comparison with H.264/Advanced Video Coding, the newest video coding standard, High Efficiency Video Coding (HEVC), improves video coding rate-distortion (RD) performance, but at the price of significant increase in its encoding complexity, especially, in intra-mode decision due to the adoption of more complex block partitions and more candidate intra-prediction modes (IPMs). To reduce the mode decision complexity in HEVC intra-frame coding, while maintaining its RD performance, in this paper, we first formulate the mode decision problem in intra-frame coding as a Bayesian decision problem based on the newly proposed transparent composite model (TCM) for discrete cosine transform coefficients, and then present an outlier-based fast intra-mode decision (OIMD) algorithm. The proposed OIMD algorithm reduces the complexity using outliers identified by TCM to make a fast coding unit split/nonsplit decision and reduce the number of IPMs to be compared. To further take advantage of the outlier information furnished by TCM, we also refine entropy coding in HEVC by encoding the outlier information first, and then the actual mode decision conditionally given the outlier information. The proposed OIMD algorithm can work with and without the proposed entropy coding refinement. Experiments show that for the all-intra-main test configuration of HEVC: 1) when applied alone, the proposed OIMD algorithm reduces, on average, the encoding time (ET) by 50% with 0.7% Bjontegaard distortion (BD)-rate increase and 2) when applied in conjunction with the proposed entropy coding refinement, it reduces, on average, both the ET by 50% and BD-rate by 0.15%.

An efficient DCT-based image compression system based on laplacian transparent composite model.

Recently, a new probability model dubbed the Laplacian transparent composite model (LPTCM) was developed for DCT coefficients, which could identify outlier coefficients in addition to providing superior modeling accuracy. In this paper, we aim at exploring its applications to image compression. To this end, we propose an efficient nonpredictive image compression system, where quantization (including both hard-decision quantization (HDQ) and soft-decision quantization (SDQ)) and entropy coding are completely redesigned based on the LPTCM. When tested over standard test images, the proposed system achieves overall coding results that are among the best and similar to those of H.264 or HEVC intra (predictive) coding, in terms of rate versus visual quality. On the other hand, in terms of rate versus objective quality, it significantly outperforms baseline JPEG by more than 4.3 dB in PSNR on average, with a moderate increase on complexity, and ECEB, the state-of-the-art nonpredictive image coding, by 0.75 dB when SDQ is OFF (i.e., HDQ case), with the same level of computational complexity, and by 1 dB when SDQ is ON, at the cost of slight increase in complexity. In comparison with H.264 intracoding, our system provides an overall 0.4-dB gain or so, with dramatically reduced computational complexity; in comparison with HEVC intracoding, it offers comparable coding performance in the high-rate region or for complicated images, but with only less than 5% of the HEVC intracoding complexity. In addition, our proposed system also offers multiresolution capability, which, together with its comparatively high coding efficiency and low complexity, makes it a good alternative for real-time image processing applications.

Transparent Composite Model for DCT Coefficients: Design and Analysis

The distributions of discrete cosine transform (DCT) coefficients of images are revisited on a per image base. To better handle, the heavy tail phenomenon commonly seen in the DCT coefficients, a new model dubbed a transparent composite model (TCM) is proposed and justified for both modeling accuracy and an additional data reduction capability. Given a sequence of the DCT coefficients, a TCM first separates the tail from the main body of the sequence. Then, a uniform distribution is used to model the DCT coefficients in the heavy tail, whereas a different parametric distribution is used to model data in the main body. The separate boundary and other parameters of the TCM can be estimated via maximum likelihood estimation. Efficient online algorithms are proposed for parameter estimation and their convergence is also proved. Experimental results based on Kullback-Leibler divergence and χ(2) test show that for real-valued continuous ac coefficients, the TCM based on truncated Laplacian offers the best tradeoff between modeling accuracy and complexity. For discrete or integer DCT coefficients, the discrete TCM based on truncated geometric distributions (GMTCM) models the ac coefficients more accurately than pure Laplacian models and generalized Gaussian models in majority cases while having simplicity and practicality similar to those of pure Laplacian models. In addition, it is demonstrated that the GMTCM also exhibits a good capability of data reduction or feature extraction-the DCT coefficients in the heavy tail identified by the GMTCM are truly outliers, and these outliers represent an outlier image revealing some unique global features of the image. Overall, the modeling performance and the data reduction feature of the GMTCM make it a desirable choice for modeling discrete or integer DCT coefficients in the real-world image or video applications, as summarized in a few of our further studies on quantization design, entropy coding design, and image understanding and management.