Motion estimation is one key point in MPEG video coding allowing the use of desired bitrates in video compression. To obtain a good tradeoff between compression ratio and quality, we need more than just the reduction of the spatial redundancy in the images: the use of techniques to exploit the temporal redundancy existing in image sequences. One of such techniques is the block matching motion estimation algorithm. In this paper different search algorithms are briefly described and compared regarding matching criteria, computational complexity and search window size. Results obtained show that, depending on the type of image, there are algorithms much simpler than the full search algorithm. First results favor the adoption of algorithms designated OSA, LBS and CSA.

Describes a video coder based on a hybrid DPCM-vector quantization algorithm that is suited for bit rates ranging from 8-16 kb/s. The proposed approach involves segmenting difference images into variable-size and variable-shape blocks and performing segmentation and motion compensation simultaneously. The purpose of obtaining motion vectors for variable-size and variable-shape blocks is to improve the quality of motion estimation, particularly in those areas where the edges of moving objects are situated. For the larger blocks, decimation takes place in order to simplify vector quantization. For very active blocks, which are always of small dimension, a specific vector quantizer has been applied, the fuzzy classified vector quantizer (FCVQ). The coding algorithm described displays good performance in the compression of test sequences at the rates of 8 and 16 kb/s; the signal-to-noise ratios obtained are good in both cases. The complexity of the coder implementation is comparable to that of conventional hybrid coders, while the decoder is much simpler in this proposal.

Vector quantization of images raises problems of complexity in codebook search and subjective quality of images. The family of image vector quantization algorithms proposed in this paper addresses both of those problems. The fuzzy classified vector quantizer (FCVQ) is based on fuzzy set theory and consists basically in a method of extracting a subcodebook from the original codebook, biased by the features of the block to be coded. The incidence of each feature on the blocks is represented by a fuzzy set that captures its (possibly subjective) nature. Unlike the classified vector quantizer (CVQ), in the FCVQ a specific subcodebook is extracted for each block to be coded, allowing a better adaptation to the block. The CVQ may be regarded as a special case of the FCVQ. In order to explore the possible correlation between blocks, an estimator for the degree of incidence of features on the block to be coded is included. The estimate is based on previously coded blocks and is obtained by maximizing a possibility; a distribution that intends to represent the subjective knowledge on the feature's possibility of occurrence conditioned to the coded blocks is used. Some examples of the application of a FCVQ coder to two test images are presented. A slight improvement on the subjective quality of the coded images is obtained, together with a significant reduction on the codebook search complexity and, when applying the estimator, a reduction of the bit rate.>