Achieving the desired visual quality is an important objective of video compression. In this paper, we present a frame-level quantization control approach for perceptual quality constrained video coding which aims to compress video at a certain perceptual quality level. We develop a general learning- based framework in which different video quality measures can be adopted. We model the rate-quality characteristic of the video using v-support vector regression with a Gaussian radial basis function as the kernel. Such a rate-quality modeling is useful for practical video coding with perceptual quality constraint. Specifically, we show how to minimize the bit rate cost and satisfy the quality constraint by exploiting the relationship between the quantization and advanced video quality measure. The advantage of our proposed approach in practical H.264/AVC video coding is demonstrated through simulations and evidenced by favorable experimental results.