In the self-similarity super resolution (SR) approach, similar examples are searched across down-scales in the image pyramid, and the computations of searching similar examples are very heavy. This makes it difficult to work in a real-time way under common software implementation. Therefore, the search process should be further accelerated at an algorithm level. Cauchy-Schwarz inequality has been used previously for fast vector quantization (VQ) encoding. The candidate patches in the search region of SR are analogous to the code-words in the VQ, and Cauchy-Schwarz inequality is exploited to exclude implausible candidate patches early. Consequently, significant acceleration of the similar patch search process is achieved. The proposed method can easily make an optimal trade-off between running speed and visual quality by appropriately configuring the bypass-threshold.

In this paper, we propose a fast multi-resolution block matching algorithm with three resolution levels (upper, middle, and lower levels) for multiple-frame motion estimation (MFME). The main concept of the algorithm is to perform a fast search while maintaining a PSNR performance similar to a full search block matching algorithm (FSBMA). The algorithm combines motion vector prediction using the spatial correlation of motion vectors and a multiple candidate search based on a multi-resolution search. To further reduce the computational complexity, we propose two temporal reduction schemes. To reduce the number of previous reference frames to be processed, the first scheme is applied to the upper level by using the information obtained from the search results of the spatio-temporally adjacent macroblocks (MBs) and the result from the current MB in the middle level of the first reference frame. The other scheme is applied to the lower level by using statistical information. Experimental results show that the proposed algorithm guarantees an average PSNR loss of less than 0.23 dB with dramatically reduced computational complexity as compared to the FSBMA. In particular, for sequences with fast motion or frame skipping, the proposed method provides a more prominent PSNR performance than those of existing fast schemes with a comparable computational complexity.

The encoding process of vector quantization (VQ) is a time bottleneck preventing its practical applications. In order to speed up VQ encoding, it is very effective to use lower dimensional features of a vector to estimate how large the Euclidean distance between the input vector and a candidate codeword could be so as to reject most unlikely codewords. The three popular statistical features of the average or the mean, the variance, and L2 norm of a vector have already been adopted in the previous works individually. Recently, these three statistical features were combined together to derive a sequential EEENNS search method in [6], which is very efficient but still has obvious computational redundancy. This Letter aims at giving a mathematical analysis on the results of EEENNS method further and pointing out that it is actually unnecessary to use L2 norm feature anymore in fast VQ encoding if the mean and the variance are used simultaneously as proposed in IEENNS method. In other words, L2 norm feature is redundant for a rejection test in fast VQ encoding. Experimental results demonstrated an approximate 10-20% reduction of the total computational cost for various detailed images in the case of not using L2 norm feature so that it confirmed the correctness of the mathematical analysis.

A fast encoding technique is described for vector quantization (VQ) of line spectral frequency parameters. A reduction in VQ encoding complexity is achieved by using a preliminary test that reduces the necessary codebook search range. The test is performed based on two criteria. One criterion uses the distance between a specific single element of the input vector and the corresponding element of the codevectors in the codebook. The other criterion makes use of the ordering property of LSF parameters. The fast encoding technique is implemented in the enhanced variable rate codec (EVRC) encoding algorithm. Simulation results show that the average searching range of the codebook can be reduced by 44.50% for the EVRC without degradation of spectral distortion (SD).

In this paper, we propose two fast codebook generation algorithms for entropy-constrained vector quantization. The first algorithm uses the angular constraint to reduce the search area and to accelerate the search process in the codebook design. It employs the projection angles of the vectors to a reference line. The second algorithm has feature of using a suitable hyperplane to partition the codebook and image data. These algorithms allow significant acceleration in codebook design process. Experimental results are presented on image block data. These results show that our new algorithms perform better than the previously known methods.

Conventional vector quantization (VQ) encoding method by full search (FS) is very heavy computationally but it can reach the best PSNR. In order to speed up the encoding process, many fast search methods have been developed. Base on the concept of multi-resolutions, the FS equivalent fast search methods using mean-type pyramid data structure have been proposed already in. In this Letter, an enhanced sum pyramid data structure is suggested to improve search efficiency further, which benefits from (1) exact computing in integer form, (2) one more 2-dimensional new resolution and (3) an optimal pair selecting way for constructing the new resolution. Experimental results show that a lot of codewords can be rejected efficiently by using this added new resolution that features lower dimensions and earlier difference check order.

A fast winner search method based on separating all codewords in the original codebook completely into a promising group and an impossible group is proposed. Group separation is realized by using sorted both L1 and L2 norms independently. As a result, the necessary search scope that guarantees full search equivalent PSNR can be limited to the common part of the 2 individual promising groups. The high search efficiency is confirmed by experimental results.

A fast winner search method for VQ based on 2-pixel-merging sum pyramid is proposed in order to reject a codeword at an earlier stage to reduce the computational burden. The necessary search scope of promising codewords is meanwhile narrowed by using sorted real sums. The high search efficiency is confirmed by experimental results.