Principios básicos y aspectos metodológicos

7.1.1 Microscopy Video Compression

For the projects that I worked on with the application of microscopy compression, I made three major contributions. First, I invented a new video compression technique that is based on adjacent- pixel over time correlation scores (Shao et al., 2015). To the best of my knowledge, this is the first video compression technique in the literature of this kind. The compression technique integrates a correlation-based video frame segmentation technique that makes use of the PSF. Second, I proposed a new way to evaluate the quality of a compressed microscopy video based on statistical tests (Shao et al., 2018). Third, the two new video compression methods I invented achieve better performance comparing to H.264 video compression standard. Having the same compressed video quality, the analysis-preserving compression achieves up to 20x better compression than H.264. The analysis-aware compression achieves up to 1000x compression. The work in microscopy compression also results in a patent: (Russell et al., 2017).

My segmentation technique can be applied to a wide variety of videos, as long as the video is taken by an optical system that involves the PSF. I tested applying my PSF-based video frame

a football field video, and the segmentation on that frame with my method. My method correctly identifies the moving part of the video. The background regions in the segmentation are the stationary football field. During a compression, these regions can be represented by a fixed football field texture.

Figure 7.1: A sample football field image taken by a human-scale camera, and processed by the PSF-based segmentation method.

7.1.2 Image Compression for BLE Beacons

I worked on three projects on compressing image data to enable image storage and broadcast via BLE broadcasting channels. My major contribution on this topic is three-fold: first, I invented the first binary image beacon system that enables 64×64 binary image storage and broadcast with BLE beacons (Shao et al., 2016a). Second, I designed and built the first color image beacon system that takes an RGB input image and compresses it into less than 200 bytes (Shao and Nirjon, 2017). This enables affordable RGB image data broadcasting via BLE. Third, I applied the VAE-GAN model to encode a 64×64 RGB image into as small as 10 bytes. This makes it possible to carry an RGB image data with one BLE broadcasting packet.

In building these image beacon systems, I designed new algorithms for my custom image compression. They include a method to reduce patch dictionary size for binary image compression, an adaptive image encoding method for color image compression, and an IMU-guided image capture process that shortens the average image capture time.

space and broadcasting capacity. The IMU-guided capture technique can be applied to other systems that involve multi-view depth estimation.

Moreover, I performed a comprehensive study to evaluate the image beacon systems with a set of criteria including system life, image quality, and the number of beacons. The study includes both theoretical derivation and empirical experiments. The experiments I conducted include: measuring the patch matching performance with different test image types (hand-written image, simple and complex geometric shapes) in binary image compression, measuring the adaptive image encoding performance with different types of color images taken indoors and outdoors, evaluating IMU- guided image capture performance on average time savings on capturing a good pair of images for depth estimation in two indoor environments with different lighting conditions, and measuring the compressed image quality under a set of embedding length settings in the VAE-GAN encoder.

7.1.3 MARBLE: Augmented Reality Application Data Compression for Bluetooth Low En- ergy Devices

The contributions from the project MARBLE include overcoming the challenge in building a robust indoor localization using BLE RSSI and the design and implementation of the first indoor sensor fusion system that uses BLE signals. The system presents a novel use of BLE beacons for both content broadcasting and localization. The system has the benefit of low power consumption. The battery-powered MARBLE system lasts seven years. The system also has low cost. The cost of building MARBLE is around $200, which is 20 times cheaper than some of the state-of-art indoor AR solutions such as Microsoft HoloLens.

I designed an ORB visual feature selection algorithm to support ORB feature broadcasting via BLE broadcasting channels for indoor localization. My feature selection algorithm outperforms existing techniques in selecting visual features for camera localization and pose estimation. This is because it takes both the uniqueness and spatial location of a visual feature into consideration. The algorithm can be generalized for selecting other types of visual features. The indoor localization technique I invented that combines BLE, IMU and camera is the first indoor localization technique

that combines these three sensor signals. It has a higher accuracy comparing to existing techniques that uses two or one of the three sensor signals. This technique can be applied to other tasks including indoor motion planning.