Regulating The Degree Of Contrast Enhancement In Global Histogram Equalization-Based Method For Grayscale Photo Processing

Global Histogram equalization (GHE) is a popular image contrast enhancement method. However, it is rarely used on photo processing because it tends to create noise-artifacts, especially in simple-structure-image. A few GHE-based methods have been proposed to address this issue but whether they are noise-artifacts-proof remains questionable. This is because the methods are fully automatic and the evaluation conducted was not comprehensive. A novel automatic GHE-based method called Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE) has been proposed in this thesis. It has been evaluated thoroughly together with the existing automatic methods. The results have proven that none of the automatic GHE-based methods is noise-artifacts-proof. The conclusion has motivated author to look into scalable GHE-based methods that allows user to regulate the degree of contrast enhancement. A novel scalable GHE-based method called Recursive Mean-Separate Histogram Equalization (RMSHE) has been proposed in this thesis. It has been evaluated thoroughly together with other two existing scalable methods - Clip Limited Adaptive HE (CLAHE) and Stark’s Adaptive HE (StarkAHE). The results of separate evaluations consistently showed that none of the three methods could effectively enhance the contrast of simple-structure-image without creating any noise-artifacts. Another novel scalable GHE-based method called Scalable Global Histogram Equalization with Selective Enhancement (SGHESE) has been developed then to overcome the limitation of the existing methods. Evaluation results showed that SGHESE could enhance the image’s contrast effectively without creating any noise-artifacts. The results of subjective evaluation involving human observer also showed that the preference level of SGHESE was significantly higher compared to those of other methods. Finally, the thesis recommends extending the study of SGHESE to color image processing because majority of the images nowadays are color images

Optimization of Motion Compensated Block-Based DCT Video Compression for Software Implementation

Internet has inspired the rapid development of wide range of network application such as Tele-Conferencing, Distance-Learning, Tele-Medicine etc, in which real time video delivery plays an important role. Due to the nature of video, which is large in size, video compression is essential in determining the practical implementation of network video application. Current video compression standards such as MPEG-l, MPEG2, H.261 and H.263 employ motion compensated DCT (Discrete Cosine Transform) block-based compression schemes and offers good compression ratio. However, it requires high processing power in order to achieve real-time processing. Therefore, optimizations are desirable especially when software implementation is preferred for its flexibility as compared to hardware implementation. This thesis focuses on ways to improve the existing solutions in the algorithmic and implementation aspects. For the algorithmic aspect, the basic principles of motion compensated DCT block-based compression scheme was studied. Then, various optimized algorithms for the two core processes in the compression, DCT and motion estimation, were reviewed and analyzed. For the implementation aspect, software-driven media processing was studied. A popular software-driven media processing's technology - MMXTM was studied for its application in 2-D 8x8 DCI. The above studies and reviews provide two proposals for improvements. The first proposal is a method based on the energy preservation theorem to be applied in the H.263 video compression standard to detect frequent All-Zero-AC coefficient blocks. When such a block was detected, some of the standard processing steps may be skipped and some computation may be saved. The proposed new algorithm was evaluated and the results indicate that it was practical in low bit rate environments targeted by H.263 as no negative speed gain was observed for the full range of step size during the evaluation. Existing MMX implementation of 2-D 8x8 IOCT with uniform 16-bit precision can hardly pass the IEEE standard compliance test, which serve to prevent Inverse DCT mismatch that can cause serious distortion in decoded video. Therefore, the second proposal suggests a standard compliance implementation with mixed 32116-bit precision and rounding. The mixed 32116-bit design has the capability to absorb the extra operations incurred by 32-bit operation through eliminating the need for matrix transposition. The proposed implementation's precision was further improved by rounding before it could pass the entire test. Result shows that the proposed implementation needed only small increment (<10%) in overall operations in order to be standard compliance

Preserving brightness in histogram equalization based contrast enhancement techniques

Histogram equalization (HE) has been a simple yet effective image enhancement technique. However, it tends to change the brightness of an image significantly, causing annoying artifacts and unnatural contrast enhancement. Brightness preserving bi-histogram equalization (BBHE) and dualistic sub-image histogram equalization (DSIHE) have been proposed to overcome these problems but they may still fail under certain conditions. This paper proposes a novel extension of BBHE referred to as minimum mean brightness error bi-histogram equalization (MMBEBHE). MMBEBHE has the feature of minimizing the difference between input and output image's mean. Simulation results showed that MMBEBHE can preserve brightness better than BBHE and DSIHE. Furthermore, this paper also formulated an efficient, integer-based implementation of MMBEBHE. Nevertheless, MMBEBHE also has its limitation. Hence, this paper further proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE). RMSHE is featured with scalable brightness preservation. Simulation results showed that RMSHE is the best compared to HE, BBHE, DSIHE, and MMBEBHE

Minimum mean brightness error bi-histogram equalization in contrast enhancement

Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extends. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a novel extension of BBHE referred to as minimum mean brightness error bi-histogram equalization (MMBEBHE) to provide maximum brightness preservation. BBHE separates the input image's histogram into two based on input mean before equalizing them independently. This paper proposes to perform the separation based on the threshold level, which would yield minimum absolute mean brightness error (AMBE - the absolute difference between input and output mean). An efficient recursive integer-based computation for AMBE has been formulated to facilitate real time implementation. Simulation results using sample image which represent images with very low, very high and medium mean brightness show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), can be properly enhanced by MMBEBHE. Besides, MMBEBHE also demonstrate comparable performance with BBHE and DSIHE when come to use the sample images show in [Yeong-Taeg Kim, February 1997] and [Yu Wan et al., October 5 1999]

Contrast-distorted image quality assessment based on curvelet domain features

Contrast is one of the most popular forms of distortion. Recently, the existing image quality assessment algorithms (IQAs) works focusing on distorted images by compression, noise and blurring. Reduced-reference image quality metric for contrast-changed images (RIQMC) and no reference-image quality assessment (NR-IQA) for contrast-distorted images (NR-IQA-CDI) have been created for CDI. NR-IQA-CDI showed poor performance in two out of three image databases, where the pearson correlation coefficient (PLCC) were only 0.5739 and 0.7623 in TID2013 and CSIQ database, respectively. Spatial domain features are the basis of NR-IQA-CDI architecture. Therefore, in this paper, the spatial domain features are complementary with curvelet domain features, in order to take advantage of the potent properties of the curvelet in extracting information from images such as multiscale and multidirectional. The experimental outcome rely on K-fold cross validation (K ranged 2-10) and statistical test showed that the performance of NR-IQA-CDI rely on curvelet domain features (NR-IQA-CDI-CvT) significantly surpasses those which are rely on five spatial domain features

An analysis of image quality assessment algorithm to detect the presence of unnatural contrast enhancement

Image contrast enhancement purposely aim the visibility of image to be increased. Most of these problems may happen after contrast enhancement: amplification of noise artifacts, saturation-loss of details, excessive brightness change and unnatural contrast enhancement. The main objective of this paper is to present an extensive review on existing Image Quality Assessment Algorithm (IQA) in order to detect the presence of unnatural contrast enhancement. Basically, the IQA used produced quality rating of the image while consistently with human visual perception. Current IQA to detect presence of unnatural contrast enhancement: Lightness Order Error (LOE), Structure Measure Operator (SMO) and Statistical Naturalness Measure (SNM). However, result of current IQA evaluation shows it may not giving consistent quality rating with human visual perception. Among three IQAs, SNM demonstrate better performance compared to LOE and SMO. But, it suffers with consistent rating for different spatial image resolution in same image content. Thus, an improvement suggested in this paper to overcome such problem occurred

Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation

Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extend. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE) to provide not only better but also scalable brightness preservation. BBHE separates the input image's histogram into two based on its mean before equalizing them independently. While the separation is done only once in BBHE, this paper proposes to perform the separation recursively; separate each new histogram further based on their respective mean. It is analyzed mathematically that the output image's mean brightness will converge to the input image's mean brightness as the number of recursive mean separation increases. Besides, the recursive nature of RMSHE also allows scalable brightness preservation, which is very useful in consumer electronics. Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), have been properly enhanced by RMSHE

Improve of contrast-distorted image quality assessment based on convolutional neural networks

Many image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for Contrast Distorted Images (CDI), e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQA-CDI). The existing NR-IQA-CDI relies on features designed by human or handcrafted features because considerable level of skill, domain expertise and efforts are required to design good handcrafted features. Recently, there is great advancement in machine learning with the introduction of deep learning through Convolutional Neural Networks (CNN) which enable machine to learn good features from raw image automatically without any human intervention. Therefore, it is tempting to explore the ways to transform the existing NR-IQA-CDI from using handcrafted features to machine-crafted features using deep learning, specifically Convolutional Neural Networks (CNN).The results show that NR-IQA-CDI based on non-pre-trained CNN (NR-IQA-CDI-NonPreCNN) significantly outperforms those which are based on handcrafted features. In addition to showing best performance, NR-IQA-CDI-NonPreCNN also enjoys the advantage of zero human intervention in designing feature, making it the most attractive solution for NR-IQA-CDI

Naive bayes-guided bat algorithm for feature selection.

When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets

Relativistic effects and two-body currents in 2H(e⃗,e′p)n^{2}H(\vec{e},e^{\prime}p)n using out-of-plane detection

Measurements of the ${^2}H(\vec{e},e^{\prime}p)n$ reaction were performed using an 800-MeV polarized electron beam at the MIT-Bates Linear Accelerator and with the out-of-plane magnetic spectrometers (OOPS). The longitudinal-transverse, $f_{LT}$ and $f_{LT}^{\prime}$, and the transverse-transverse, $f_{TT}$, interference responses at a missing momentum of 210 MeV/c were simultaneously extracted in the dip region at Q$^2$=0.15 (GeV/c)$^2$. On comparison to models of deuteron electrodisintegration, the data clearly reveal strong effects of relativity and final-state interactions, and the importance of the two-body meson-exchange currents and isobar configurations. We demonstrate that these effects can be disentangled and studied by extracting the interference response functions using the novel out-of-plane technique.Comment: 4 pages, 4 figures, and submitted to PRL for publicatio