Perceptually based quantization technique for MPEG encoding

We present a technique for controlling the adaptive quantization process in an MPEG encoder, which improves upon the commonly used TM5 rate controller. The method combines both a spatial masking model and a technique for automatically determining the visually important areas in a scene. The spatial masking model has been designed with consideration of the structure of compressed natural images. It takes into account the different levels of distortion that are tolerable by viewers in different parts of a picture by segmenting the scene into flat, edge, and textured regions and quantizing these regions differently. The visually important scene areas are calculated using Importance Maps. These maps are generated by combining factors known to influence human visual attention and eye movements. Lower quantization is assigned to visually important regions, while areas classified as being of low visual importance are more harshly quantized. Results indicate a subjective improvement in picture quality, in comparison to the TM5 method. Less ringing occurs at edges, and the visually important areas of a picture are more accurately coded. This is particularly noticeable at low bit rates. The technique is computationally efficient and flexible, and can easily be extended to specific applications.