Classification-based macroblock layer rate control for low delay transmission of H.263 video

Puri and Aravind's method of macroblock bit count estimation for video rate control is based on the classification of the macroblock data into discrete classes and assigning a unique nonlinear estimate for each class and quantization parameter pair. This method stands apart from other methods in the literature, since the model of the bit count versus the quantization parameter relation, parameterized by macroblock variance, is a discrete model generated solely from measurements. We extend their technique for low-delay video rate control (tight buffer regulation) in two ways. We propose a strategy of near-uniform quantization parameter assignments to the macroblocks of a frame that can come close to maximizing an objective spatial quality function, such as PSNR, over the entire frame. We also adaptively update the quantization parameter assignments for the yet to be coded macroblocks, after the encoding of each macroblock, to compensate for any errors in the bit count estimation of the encoded macroblock. Our experiments demonstrate that the proposed rate control method can more accurately control the number of bits expended for a frame, as well as yield a higher objective spatial quality than the method adopted by TMN8.