Improved Transform Methods for Low Complexity , High Quality Video Coding
چکیده
A video signal requires a very large number of bits if it is represented in an uncompressed form. This makes many video applications not possible, due to the limited bandwidth of communication channels and the limit of the storage medium. Therefore, video coders that compress the video signal so that it could be represented with a smaller number of bits are used in all digital video applications. The goal of all video coders is to maximize the video quality while minimizing the bitrate. This is achieved by exploiting different redundancies present in the video signal. One type of redundancy is present between neighboring samples in the residual frame (spatial redundancy). The focus of this thesis is to develop algorithms to exploit the spatial redundancy with the goal of improving coding efficiency of video coders while keeping their implementation complexity as low as possible. The first part of this thesis introduces fixed-point design methodologies and several resulting implementations of the inverse discrete cosine transform (IDCT). The overall architecture used in the design of the ISO/IEC 23002-2 IDCT algorithm, which can be characterized by its separable and scaled features, is also described. The drift characteristic for the integer IDCT approximations is also analyzed. The second part of the thesis discusses pre-scaled integer transform (PIT) which reduces the implementation complexity of the conventional integer cosine transform (ICT) and maintains all the merits such as bit-exact implementation and good coding efficiency. Design rules that lead to good PIT kernels are developed and different types of PIT and their target applications are examined. The PIT kernels used in Audio Video coding Standard (AVS), the Chinese National Coding standard, are also introduced. In the third part of the thesis we discussed a novel algorithm called spatially varying transform (SVT). Unlike state-of-art video codecs where the position of the transform block is fixed, SVT enables video coders to vary the position of the transform block. In addition to changing the position of the transform block, the size of the transform can also be varied within the SVT framework, in order to better localize the prediction error so that the underlying correlations are better exploited. It is shown that by varying the position of the transform block and its size, characteristics of prediction error are better localized, and the coding efficiency is thus improved. A novel low complexity algorithm, operating on a macroblock and a block level, is also proposed to reduce the encoding complexity of SVT. An extension of SVT, called Prediction Signal Aided Spatially Varying Transform (PSASVT) is also discussed, which utilizes the gradient of prediction signal to eliminate the unlikely location parameters (LPs). As the number of candidate LPs is reduced, a