Perceptual Video Quality Preservation and Enhancement
The perceptual quality of videos has attracted much attention, because it is hard to estimate by objective metrics, and because it is affected by many conditions. Compression, transmission and viewing conditions all impact the perceptual quality. In this dissertation, we aim to preserve and enhance the perceptual quality in different cases.
The impact of ambient illumination on the perceptual quality of traditional 8-bit 2D video is first studied. Some details, especially those in dark areas of videos, are invisible in bright ambient light, because of the reflection of ambient light and the reduction of the sensitivity of human eyes. We analyze the display characteristics and human visual sensitivity, and propose methods to enhance the contrast and details without increasing the peak brightness of the display.
Another viewing condition, viewing distance, is also investigated in this dissertation. A display device held farther away may have fewer details visible compared to a device held closer. The unnoticeable details can be filtered before compression, which can reduce the bit-rate of the video. A subjective test was conducted to demonstrate the bit-rate saving without degrading the perceptual quality.
Besides the traditional 8-bit videos, a new form of video, high dynamic range (HDR) videos, is studied in Chapter 4 of this dissertation. There can be banding artifacts in the inverse tone mapped HDR videos which degrade the perceptual quality, though the impact on the objective quality is subtle. An enhancement filter is proposed to remove the banding artifacts and reduce compression artifacts, and at the same time, preserve true edges and details. The parameters of the filter are determined by minimizing the proposed perceptual distortion metric.
The perceptual quality of 3D video is explored in Chapter 5. In particular, the stereoscopic 3D video of the 2D+depth format is studied. Transmission of such videos through networks can be affected by packet losses. The importance of packets is investigated by a human observer experiment. A prediction model of the importance is developed using features such as the video type, frame type, and spatial location of the packets.