1D Transforms of the Motion Compensation Residual
1-D
Transforms for the Motion Compensation Residual
SYNOPSIS
ABSTRACT:
Transforms used in image
coding are also commonly used to compress prediction residuals in video coding.
Prediction residuals have different spatial characteristics from images, and it
is useful to develop transforms that are adapted to prediction residuals. In
this paper, we explore the differences between the characteristics of images
and motion compensated prediction residuals by analyzing their local
anisotropic characteristics and develops transforms adapted to the local
anisotropic characteristics of these residuals.
The analysis indicates
that many regions of motion compensated prediction residuals have 1-D
anisotropic characteristics and we propose to use 1-D directional transforms
for these regions. We present experimental results with one example set of such
transforms within the H.264/AVC codec and the results indicate that the
proposed transforms can improve the compression efficiency of motion
compensated prediction residuals over conventional transforms.
EXISTING SYSTEM:
AN
important component of image and video compression systems is a transform. A
transform is used to transform image intensities. A transform is also used to
transform prediction residuals of image intensities, such as the motion
compensation (MC) residual, the resolution enhancement residual in scalable
video coding, or the intra prediction residual in H.264/AVC. Typically, the
same transform is used to transform both image intensities and prediction
residuals. For example, the 2-D discrete cosine transform (2-D DCT) is used to
compress image intensities in the JPEG standard and MC-residuals in many video
coding standards.
Disadvantages:
Less Compression,
Bigger File,
Loss of visual accuracy
PROPOSED SYSTEM:
An example is the 2-D discrete wavelet transform
(2-D DWT), which is used to compress images in the JPEG2000 standard and
high-pass prediction residual frames in interface wavelet coding . However,
prediction residuals have different spatial characteristics from image
intensities. It is of interest, therefore, to study if transforms better than
those used for image intensities can be developed for prediction residuals.
Recently, new transforms have been developed
that can take advantage of locally anisotropic features in images. Conventional
transform, such as the 2-D DCT or the 2-D DWT is carried out as a
separable transform by cascading two 1-Dtransforms in the vertical and
horizontal dimensions.
Advantages:
Higher Compression Ratios,
Small Files suitable for low
powered devices,
Ease of transfer due to small size
SYSTEM REQUIREMENTS:
HARDWARE MINIMUM REQUIREMENTS:
PROCESSOR : PENTIUM
IV 3.0 GHz
RAM : 2
GB
MONITOR : 19”
HARD
DISK : 80
GB
CDDRIVE : 52X
SOFTWARE REQUIREMENTS:
FRONT
END : C#
.Net , VS 2008
FRAMEWORK
USED : .net 2.0
OPERATING SYSTEM: WINDOWS XP
Applications
1.
Image and video compression
2.
Transform image intensities
3. Transform
prediction residuals of image intensities
