Low-complexity high prediction accuracy visual quality metrics and their applications in H.264/AVC encoding mode decision process

In this thesis, we develop a new general framework for computing full reference image quality scores in the discrete wavelet domain using the Haar wavelet. The proposed framework presents an excellent tradeoff between accuracy and complexity. In our framework, quality metrics are categorized as either map-based, which generate a quality (distortion) map to be pooled for the final score, e.g., structural similarity (SSIM), or non map-based, which only give a final score, e.g., Peak signal-to-noise ratio (PSNR). For mapbased metrics, the proposed framework defines a contrast map in the wavelet domain for pooling the quality maps. We also derive a formula to enable the framework to automatically calculate the appropriate level of wavelet decomposition for error-based metrics at a desired viewing distance. To consider the effect of very fine image details in quality assessment, the proposed method defines a multi-level edge map for each image, which comprises only the most informative image subbands. To clarify the application of the framework in computing quality scores, we give some examples showing how the framework can be applied to improve well-known metrics such as SSIM, visual information fidelity (VIF), PSNR, and absolute difference. We compare the complexity of various algorithms obtained by the framework to the Intel IPP-based H.264 baseline profile encoding using C/C++ implementations. We evaluate the overall performance of the proposed metrics, including their prediction accuracy, on two well-known image quality databases and one video quality database. All the simulation results confirm the efficiency of the proposed framework and quality assessment metrics in improving the prediction accuracy and also reduction of the computational complexity. For example, by using the framework, we can compute the VIF at about 5% of the complexity of its original version, but with higher accuracy. In the next step, we study how H.264 coding mode decision can benefit from our developed metrics. We integrate the proposed SSEA metric as the distortion measure inside the H.264 mode decision process. The H.264/AVC JM reference software is used as the implementation and verification platform. We propose a search algorithm to determine the Lagrange multiplier value for each quantization parameter (QP). The search is applied on three different types of video sequences having various motion activity features, and the resulting Lagrange multiplier values are tabulated for each of them. Based on our proposed Framework we propose a new quality metric PSNRA, and use it in this part (mode decision). The simulated rate-distortion (RD) curves show that at the same PSNRA, with the SSEA-based mode decision, the bitrate is reduced about 5% on average compared to the conventional SSE-based approach for the sequences with low and medium motion activities. It is notable that the computational complexity is not increased at all by using the proposed SSEA-based approach instead of the conventional SSE-based method. Therefore, the proposed mode decision algorithm can be used in real-time video coding

Optimization of the Mechanical Properties and the Cytocompatibility for the PMMA Nanocomposites Reinforced with the Hydroxyapatite Nanofibers and the Magnesium Phosphate Nanosheets

Commercial poly methyl methacrylate (PMMA)-based cement is currently used in the field of orthopedics. However, it suffers from lack of bioactivity, mechanical weakness, and monomer toxicity. In this study, a PMMA-based cement nanocomposite reinforced with hydroxyapatite (HA) nanofibers and two-dimensional (2D) magnesium phosphate MgP nanosheets was synthesized and optimized in terms of mechanical property and cytocompatibility. The HA nanofibers and the MgP nanosheets were synthesized using a hydrothermal homogeneous precipitation method and tuning the crystallization of the sodium-magnesium-phosphate ternary system, respectively. Compressive strength and MTT assay tests were conducted to evaluate the mechanical property and the cytocompatibility of the PMMA-HA-MgP nanocomposites prepared at different ratios of HA and MgP. To optimize the developed nanocomposites, the standard response surface methodology (RSM) design known as the central composite design (CCD) was employed. Two regression models generated by CCD were analyzed and compared with the experimental results, and good agreement was observed. Statistical analysis revealed the significance of both factors, namely, the HA nanofibers and the MgP nanosheets, in improving the compressive strength and cell viability of the PMMA-MgP-HA nanocomposite. Finally, it was demonstrated that the HA nanofibers of 7.5% wt and the MgP nanosheets of 6.12% wt result in the PMMA-HA-MgP nanocomposite with the optimum compressive strength and cell viability