An adaptive quantization algorithm for video coding

This paper proposes an adaptive quantization algorithm for video coding using the information obtained from the previously encoded image. Before quantizing the discrete cosine transform coefficients, the properties of reconstruction error of each macro block (MB) are estimated from the previous frame. For the estimation of the error of current MB, a block with the size of MB in the previous frame is chosen. Since the original and reconstructed images of the previous frame are available in the encoder, we can evaluate the tendency of reconstruction error of this block in advance. Then, this error is considered as the expected error of the current MB if it is quantized with the same step size and bit rate. Comparing the error of the MB with the average of overall MBs, if it is larger than the average, a small step size is given for this MB, and vice versa. As a result, the error distribution of the MB is more concentrated to the average, yielding low variance and improved image quality. Especially for low bit applications, the proposed algorithm yields much smaller error variance and higher peak signal-to-noise ratio compared to the conventional TM5. We also propose a modified algorithm for efficient hardware implementation.