This paper presents a novel approach to pixel decimation for motion estimation in video coding. Early techniques of pixel decimation use regular pixel patterns to evaluate matching criterion. Recent techniques use adaptive pixel patterns and have achieved better efficiency. However, these adaptive techniques require an initial division of a block into a set of uniform regions and therefore are only locally-adaptive in essence. In this paper, we present a globally-adaptive scheme for pixel decimation, in which no regions are fixed at the beginning and pixels are selected only if they have features important to the determination of a match. The experiment results show that when no more than 40 pixels are selected out of a 1616 block, this approach achieves a better search accuracy by 13-22% than the previous locally-adaptive methods which also use features.

In fractal image coding, for each range block, the best matching domain block is identified, and information from the best matching domains and range blocks are transmitted to the decoder for image reconstruction. In this paper, the similarity between range blocks and domain blocks is evaluated according to their centers of gravity. The number of searched domain blocks are reduced by limiting the candidates for the best matching domain blocks to those domain blocks whose similarity to the range block are high. Using simulation experiments, the number of candidates for the best matching domain blocks were reduced to about 10-23% of the current method. Thus, our proposed method had significantly reduced the number of searched domain blocks below the current method and at the same time it turns out that degradation of the reconstructed image was seldom observed.

Conventional fast block-matching algorithms, such as TSS and DSWA/IS, are widely used for motion estimation in the low-bit-rate video coding. These algorithms are based on the assumption that when searching in the previous frame for the block that best matches a block in the current frame, the difference between them increases monotonically when a matching block moves away from the optimal solution. Unfortunately, this assumption of global monotonicity is often not valid, which can lead to a high possibility for the matching block to be trapped to local minima. On the other hand, monotonicity does exist in localized areas. In this paper, we proposed a new algorithm called Peano-Hilbert scanning search algorithm (PHSSA). With the Peano-Hilbert image representation, the assumption of global monotonicity is not necessary, while local monotonicity can be effectively explored with binary search. PHSSA selects multiple winners at each search stage, minimizing the possibility of the result being trapped to local minima. The algorithm allows selection of three parameters to meet different search accuracy and process speed: (1) the number of initial candidate intervals, (2) a threshold to remove the unpromising candidate intervals at each stage, and (3) a threshold to control when interval subdivision stops. With proper parameters, the multiple-candidate PHSSA converges to the optimal result faster and with better accuracy than the conventional block matching algorithms.

In this paper, we present an image compression algorithm using two concepts, subdividing an image matrix and stratifying submatrices into FD-submatrices (feature distribution submatrices). According to the feature distribution and the view that an image can be decomposed into some feature layers, we generate a compression tree by setting up a logic process of decomposition and stratification. To get better compression ratios, the set of submatrices having one and zero as elements, including logic flag sequences is compressed by vector space theory.

In DCT transform coding it is usually necessary to discard some of the ac coefficients obtained after the transform operation for data compression reasons. Although most of the energy is usually compacted in the few coefficients that are transmitted, there are many instances where the discarded coefficients contain significant information. The absence of these coefficients at the decoder can lead to visible degradation of the reconstructed image especially around slow moving objects. We propose a simple but effective method which uses an indirect form of vector quantization to supplement scalar quantization in the transform domain. The distribution pattern of coefficients that fall below a fixed threshold is vector quantized and an index of the pattern chosen from a codebook is transmitted together with two averages; one for the positive coefficients and the other for negative coefficients. In the reconstruction, the average values are used instead of setting the corresponding coefficients to zero. This is tantamount to quantizing the mid range frequency coefficients with 1 bit but the resulting bit-rate is much less. We aim to propose an alternative to using traditional vector quantization which entails computational complexities and large time and memory requirements.

We propose an automatic generative method for stylus style CG as automatic generative method for non-photorealistic CG.