A spatial scalable video coding with selective data transmission using wavelet decomposition.

Abstract

In this research a scalable video coding framework is proposed, mainly focusing on spatial scalability, and a subjective data compression algorithm based on: (1) quality, (2) resolution (target output device), and (3) bandwidth. This framework enables the scalable delivery of video based on the output display resolution, and through a congested network or limited bandwidth with an acceptable visual quality. In order to achieve this scalable framework we have used wavelets, for greater flexibility, and a multiresolution approach. The multiresolution motion estimation (MRME) provides the reusability of motion vectors across different resolution levels. In MRME the motion estimation, which is carried out in the wavelet domain, is initially performed in the lower resolution and the resultant motion vectors are used as a basic motion estimate in other higher resolutions. The translation of motion vectors across different resolution levels results in translation error or mismatches. These mismatches are identified using a novel approach, which uses two thresholds. The first threshold is used to determine the possible occurrence of mismatches in a given video frame subject to the motion in the previous frame. This helps to give a broader location of all the mismatches in general. In order to specifically focus on the worst mismatches among them another threshold is used. This gives a more accurate identification of the mismatches that definitely need to be handled while the others can be waived depending upon the available resources. By varying these two parameters, the quality and resolution of the video can be adjusted to suit the bandwidth requirements. The next step is about handling the identified mismatches. The refinements are handled in any of the following two ways: by using motion vector correction, which gives improved prediction, or by using the directly replacing the error block. We have also presented a brief comparative study of the two error correction methods, discussing their benefits and drawbacks. The methods used here give a precise motion estimate thereby utilizing the temporal redundancy in an efficient manner and providing an effective scalability solution. This scalable framework is useful to provide a flexible multiresolution adaptation to various network and terminal capabilities, to provide quality degradation during severe network conditions, and to provide better error robustness.