We consider the perceptual quality optimization in image coding through adaptive quantization. A differential contrast model is proposed to measure the visual sensitivity to the quantization distortions, and thereby deriving the spatially adaptive quantization strategy. A complementary quantitative approach is provided as a means to efficiently calculate the proposed differential contrast model. The resulting visual quality improvement is experimentally demonstrated.

Three-dimensional (3D) vision based scanning for metrology and inspection applications is an area that has attracted increasing interest in the industry. This interest is driven by the recent advances in 3D technologies, which enable high precision measurements at an affordable cost. 3D vision allows for the modelling and inspection of the visible surface of objects. When it is necessary to detect subsurface defects, active infrared (IR) thermography is one of the most commonly used tools today for the non-destructive testing and evaluation (NDT&E) of materials. Fusion of these two modalities allows the simultaneous detection of surface and subsurface defects and the visualisation of these defects overlaid on the 3D model of the scanned and modelled parts or their 3D computer-aided design (CAD). In this work, we present a framework for automatically fusing 3D data (scanned or CAD) with the infrared thermal images for an NDT&E process in 3D space. The captured 3D images and their thermal infrared counterparts are aligned and fused using automatically detected features. This fusion is undergone on 3D space, thus allowing 3D visualisation of subsurface defects on a 3D model (or CAD). Additionally, the defects are extracted using image processing techniques and overlaid over their virtual position in 3D space. Their positioning at a certain distance from the part's 3D surface is proportional to the computed depth using phase image analysis in the Fourier domain. This depth represents the real position of the detected subsurface defect and is extracted using thermograms (temporal sequence of thermal images). The results obtained are promising and show how this new technology can be used efficiently in a combined NDT&E-Metrology analysis of manufactured parts, in areas such as aerospace and automotive, among others.

Three-dimensional (3D) vision scanning for metrology and inspection applications is an area that knows an increasing interest in the industry. This interest is driven by the recent advances in 3D technologies, permitting to attain high precision measurements at an affordable cost. 3D vision allows for the modelling and inspection of the visible surface of objects. When it is necessary to detect subsurface defects, active infrared (IR) thermography is one of the most used tools today for non-destructive testing (NDT) of materials. Fusion of these two modalities allows the simultaneous detection of surface and subsurface defects and to visualize these defects overlaid on a 3D model of the scanned and modelled parts or their 3D computer-aided design (CAD) models. In this work, we present a framework for automatically fusing 3D data (scanned or CAD) with the infrared thermal images for an NDT process in 3D space.