Tuesday, July 10, 2012

1D Transforms of the Motion Compensation Residual

1-D Transforms for the Motion Compensation Residual


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.


            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.

            Less Compression,
Bigger File,
            Loss of visual accuracy


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.

            Higher Compression Ratios,
Small Files suitable for low powered devices,
            Ease of transfer due to small size

PROCESSOR        :    PENTIUM IV 3.0 GHz
RAM                      :    2 GB
MONITOR             :    19”
HARD DISK         :     80 GB
CDDRIVE              :    52X

FRONT END                  :    C# .Net , VS 2008
FRAMEWORK USED    :    .net 2.0

1.      Image and video compression
2.      Transform image intensities
3.      Transform prediction residuals of image intensities