ROI Based Post Image Quality Assessment Technique on Multiple Localized Filtering Method On Kinect HD Sensor Mobile Robot

Author(s):  
Kholilatul Wardani ◽  
Aditya Kurniawan

 The ROI (Region of Interest) Image Quality Assessment is an image quality assessment model based on the SSI (Structural Similarity Index) index used in the specific image region desired to be assessed. Output assessmen value used by this image assessment model is 1 which means identical and -1 which means not identical. Assessment model of ROI Quality Assessment in this research is used to measure image quality on Kinect sensor capture result used in Mobile HD Robot after applied Multiple Localized Filtering Technique. The filter is applied to each capture sensor depth result on Kinect, with the aim to eliminate structural noise that occurs in the Kinect sensor. Assessment is done by comparing image quality before filter and after filter applied to certain region. The kinect sensor will be conditioned to capture a square black object measuring 10cm x 10cm perpendicular to a homogeneous background (white with RGB code 255,255,255). The results of kinect sensor data will be taken through EWRF 3022 by visual basic 6.0 program periodically 10 times each session with frequency 1 time per minute. The results of this trial show the same similar index (value 1: identical) in the luminance, contrast, and structural section of the edge region or edge region of the specimen. The value indicates that the Multiple Localized Filtering Technique applied to the noise generated by the Kinect sensor, based on the ROI Image Quality Assessment model has no effect on the image quality generated by the sensor.

Author(s):  
Yuan-Yuan Fan ◽  
Ying-Jun Sang

On the basis of the research status of image quality comprehensive assessment, a no-reference image quality comprehensive assessment function model is proposed in this paper. First, the image quality is classified as contrast, sharpness, and signal-to-noise ratio (SNR), and the interrelation of each assessment index is researched and analyzed; second, the weights in the comprehensive assessment model are studied when only contrast, sharpness, and SNR are changed. Finally, on the basis of studying each kind of distortion separately, and considering the different types of image distortion, we studied how to determine the weights of each index in the comprehensive image quality assessment. The results show that the no-reference image quality comprehensive assessment function model proposed in this paper can better fit human visual perception, and it has a good correlation with Difference Mean Opinion Score (DMOS). Correlation Coefficient (CC) reached 0.8331, Spearman Rank Order Correlation Coefficient (SROCC) reached 0.8206, Mean Absolute Error (MAE) was only 0.0920, Root Mean Square Error (RMSE) was only 0.1122, Outlier Ratio (OR) was only 0.0365. The method proposed in this paper can be applied to photoelectric measurement equipment television system and give an accurate and reliable quality assessment to no reference television images.


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