This paper reviews the perception of image quality by human respondents in the context of super-resolution.
Specifically, the desirability of an automated mechanism for generating a metric that correlates well with human
perceptions of image quality is discussed. Common metrics are evaluated to ascertain whether they demonstrate suitable
correlation with human perception of image quality. It is found that the commonly used pixel-difference evaluation
technique outperforms a threshold-based technique; however, neither is demonstrated to correlate well with human
The requirement to transmit video data over unreliable wireless networks (with the possibility of packet loss) is
anticipated in the foreseeable future. Significant compression ratio and error resilience are both needed for complex
applications including tele-operated robotics, vehicle-mounted cameras, sensor network, etc. Block-matching based
inter-frame coding techniques, including MPEG-4 and H.264/AVC, do not perform well in these scenarios due to error
propagation between frames. Many wireless applications often use intra-only coding technologies such as Motion-JPEG,
which exhibit better recovery from network data loss at the price of higher data rates. In order to address these research
issues, an intra-only coding scheme of H.264/AVC (iAVC) is proposed. In this approach, each frame is coded
independently as an I-frame. Frame copy is applied to compensate for packet loss. This approach is a good balance
between compression performance and error resilience. It achieves compression performance comparable to Motion-
JPEG2000 (MJ2), with lower complexity. Error resilience similar to Motion-JPEG (MJ) will also be accomplished.
Since the intra-frame prediction with iAVC is strictly confined within the range of a slice, memory usage is also
extremely low. Low computational complexity and memory usage are very crucial to mobile stations and devices in
Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, which can result in inaccurate image analysis. Blurring is considered a serious problem. Therefore, "deblurring" an image to obtain better quality is an important issue in medical image processing. In our research, the image is initially decomposed. Contrast improvement is achieved by modifying the coefficients obtained from the decomposed image. Small coefficient values represent subtle details and are amplified to
improve the visibility of the corresponding details. The stronger image density variations make a major contribution to the overall dynamic range, and have large coefficient values. These values can be reduced without much information loss.