Technical Program

Paper Detail

Paper:	IMDSP-P17.4
Session:	Image Quality Assessment and Enhancement
Time:	Friday, May 19, 16:30 - 18:30
Presentation:	Poster
Topic:	Image and Multidimensional Signal Processing: Image Quality Assessment
Title:	Spatial Resolution and Quantization Noise Tradeoffs for Scalable Image Compression
Authors:	Soo Hyun Bae, Georgia Institute of Technology, United States; Thrasyvoulos N. Pappas, Northwestern University, United States; Biing-Hwang Juang, Georgia Institute of Technology, United States
Abstract:	Most full-reference quality metrics compare the original image to a distorted image at the same level of resolution assuming a fixed viewing distance. In video streaming applications, however, the transmitted signal may differ from the original in compression as well as spatiotemporal resolution. For example, at low bitrate coding applications the compressed image may be too distorted, and hence the observer may prefer to reduce the resolution or increase the viewing distance in order to reduce the visibility of the compression artifacts. The selection of the best tradeoff between resolution/viewing distance and visibility of compression artifacts requires a quality metric that accounts for both image distortions and image size. Such tradeoffs are not reflected in existing quality metrics, which ignore the signal visibility and only measure the visibility of compression distortions, which decrease with image size. In order to better understand such tradeoffs, with the goal of developing better quality metrics, we conducted subjective tests using a number of existing still image coders (JPEG2000, SPIHT, and JPEG). Our results indicate that objective quality (perceptually weighted PSNR) of the images that the viewers select decreases with resolution, that is, the viewers are willing to accept more artifacts as image size decreases.