Reconstruction of the Image Metric of Periodic Structures in an Opto-Digital Angle Measurement System

Measurement of the object angular position and its change is one of the important tasks in measurement technique. Our method is based on determination of the angular position of a 2D periodical optical pattern (2D mark) at the object, captured by the sensor of a digital camera. System performance can be frustrated by errors in determination of the spot coordinates on the camera sensor; by the presence of lens aberrations; by deviations from the parallelism of the pattern planes and the camera sensor; and by differences between the actual spots positions and the ideal grid. In the paper we discuss the effect of these errors and the way to correct or eliminate them. We have developed the mathematical routine and the corresponding numerical codes for correction of the said errors. The code and the routine we checked in a real experiment. It has shown that the correction decreases the standard deviation in 15 times.

ADAPTIVE POWER CONTROL ON DVB-T SYSTEM BASED ON NO-REFERENCE PERCEPTUAL PICTURE QUALITY METRIC

Digital Video Broadcasting – Terrestrial (DVB-T) adalah salah satu standar broadcast televisi digital di eropa. Ketika mentransmisikan data melalui sistem DVB-T, noise merupakan masalah yang tidak dapat dihindarkan. Masalah tersebut dapat menyebabkan penurunan kualitas video yang diterima oleh end user. Berdasarkan penjelasan diatas, penulis mendesain sebuah adaptive power control on DVB-T system based-on no-reference perceptual picture quality metric yang dapat menentukan berapa banyak transmit power yang dibutuhkan oleh receiver untuk mendapatkan hasil kualitas video yang baik. No-reference image metric akan digunakan untuk membuat visual quality feedback di sisi receiver. Hasil dari image metric ini berupa nilai dalam bentuk angka yang akan dikirimkan ke transmitter sebagai feedback. Di sisi transmitter, ada explicit link adaptation yang mengatur power menggunakan perceptual quality (PQ-based) berdasarkan visual quality feedback. Hasil dari adaptive power control menunjukkan bahwa, ketika visual quality feedback menunjukkan angka dibawah 96 poin ( unacceptable quality ), power akan dinaikkan sebesar 0.05 dB untuk meningkatkan kualitas video yang diterima. Sebaliknya, jika visual quality feedback menunjukkan lebih dari sama dengan 96 ( acceptable quality ), power akan diturunkan untuk menghemat biaya power cost. Adaptive power control memberikan hasil yang sangat baik karena secara signifikan dapat mengurangi nilai visual quality yang buruk pada simulasi dengan menggunakan power sebesar 18 dB.

Exploring the efficiency of image metric for assessing the visual quality of 3D mesh model

Recent developments in 3D graphics technology have led to extensive processes on 3D meshes (e.g., compression, simplification, transmission and watermarking), these processes unavoidably cause the visual perceptual degradation of the 3D objects. The existing mesh visual quality evaluation metrics either require topology constrain or fail to reflect the perceived visual quality. Meanwhile, for the 3D objects that are observed on 2D screens by the users, it is reasonable to apply image metric to assess the distortion caused by mesh simplification. We attempt to explore the efficiency of image metric for assessing the visual fidelity of the simplified 3D model in this paper. For this purpose, several latest and most effective image metrics, 2D snapshots, number and pooling algorithms are involved in our study, and finally tested on the IEETA simplification database. The statistical data allow the researcher to select the optimal parameter for this image-based mesh visual quality assessment and provide a new perspective for the design and performance assessment of mesh simplification algorithms.

Developing Image Processing Meta-Algorithms with Data Mining of Multiple Metrics

People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation.