Reducing Inverse Quantization Numbers for Video Transcoding

In this paper, a complexity-quality analysis with transcoding architectures is proposed for reduced inverse quantization numbers. This architecture is different from conventional transcoding scheme, which neglects the relation between first and second quantized step size. However, our proposed transcoding architecture depends on the modulus of the ratio of second quantization and first quantization parameter. By analyzing quantized area of first quantization and second quantization parameter, we concluded the part of undoing first inverse quantization, to reduce computing complexity. From computer simulation we verify the merits of the proposed scheme over the conventional transcoding approaches, in terms of achieving better performance based on the computing complexity and objective (e.g., the peak signal-to-noise ratio) analysis.