This paper presents a novel method of reference frame compression for use in H.264/AVC video codecs. The method comprises a predictive pattern decision for encoding of original 2x2 pixel blocks, selective step quantization and fast reconstruction of embedded patterns during the decoding. The proposed approach has a number of advantages over existing methods, such as random access to compressed data, a constant compression rate of 50% and low cost hardwarefriendly implementation requiring less than 7k gates. Experimental results indicate an acceptable quality loss of 0.47dB on average for video resolutions from CIF to 720p and only 5-15% increase in total computational complexity for the software implementation.

No abstract

The H.264 video encoding standard can achieve high coding efficiency at the expense of high computational complexity. Typically, real-time software implementation requires omission of most optional encoding tools leading to significantly reduced coding efficiency. This paper proposes a novel method for realtime H.264 encoding based on dynamic control of the encoding parameters to meet real-time constraints while minimizing coding efficiency loss. Experimental results show that the method provides up to 19% lower bit rate than conventional real-time encoding using fixed parameters with the same visual quality. The method allows real-time 30fps QCIF encoding on a Pentium IV with similar coding efficiency to full search baseline profile encoding.

This paper presents a novel real-time algorithm for reducing and dynamically controlling the computational complexity of an H.264 video encoder implemented in software. A fast Mode Decision algorithm, based on a Pareto optimal MacroBlock classification scheme, is combined with a Dynamic Complexity Control algorithm that adjusts the MB Class decisions such that a constant frame rate is achieved. The average coding efficiency of the proposed algorithm was found to be similar to that of conventional encoding operating at half the frame rate. The proposed algorithm was found to provide lower average bit rate and distortion than Static Complexity Scaling.