Cheng‐Hsiung Hsieh, Ching‐Hua Liu, Kuan‐Chieh Hsiung and Qiangfu Zhao
The purpose of this paper is to solve the pack loss problem of video transmitted over error‐prone channels. Pack loss generally affects the visual quality of reconstructed frames…
Abstract
Purpose
The purpose of this paper is to solve the pack loss problem of video transmitted over error‐prone channels. Pack loss generally affects the visual quality of reconstructed frames significantly. Consequently, a grey approach to error concealment (EC) is proposed.
Design/methodology/approach
Note that missing information in error blocks can be found before and after the error frame. Thus, two adjacent error‐free frames are utilized to conceal error blocks caused by packet loss. This paper presents an EC approach based on grey polynomial interpolation (GPI) which is called the GTEC. In the GTEC, the following stages are involved. First, error blocks due to packed loss are detected. Then, optimal reference blocks in adjacent frames are found through boundary matching algorithm (BMA). Finally, estimated blocks are obtained by the GPI. By replacing error blocks with the estimated blocks, EC is completed in the GTEC.
Findings
In the simulation, the proposed GTEC is compared with the EC scheme in H.264 and the BMA. With packet loss rates of 1, 3, 5, and 10 per cent, the proposed GTEC approach has better performance than EC schemes in H.264 and BMA, both in peak signal‐to‐noise ratio and visual quality. Consequently, it provides an alternative where EC is required.
Originality/value
The value of GTEC proposed in this paper is not only in better performance but also in the originality to apply grey scheme, i.e. GPI, in the field of EC.
Details
Keywords
In recent years, principal component analysis (PCA) has attracted great attention in dimension reduction. However, since a very large transformation matrix must be used for…
Abstract
Purpose
In recent years, principal component analysis (PCA) has attracted great attention in dimension reduction. However, since a very large transformation matrix must be used for reconstructing the original data, PCA has not been successfully applied to image compression. To solve this problem, this paper aims to propose a new technique called k‐PCA.
Design/methodology/approach
Actually, k‐PCA is a combination of vector quantization (VQ) and PCA. The basic idea is to divide the problem space into k clusters using VQ, and then find a PCA encoder for each cluster. The point is that if the k‐PCA encoder is obtained using data containing enough information, it can be used as a semi‐universal encoder to compress all images in a given domain.
Findings
Although a k‐PCA encoder is more complex than a single PCA encoder, the compression ratio can be much higher because the transformation matrices can be excluded from the encoded data. The performance of the k‐PCA encoder can be improved further through learning. For this purpose, this paper‐proposes an extended LBG algorithm.
Originality/value
The effectiveness of the k‐PCA is demonstrated through experiments with several well‐known test images.