Prediction and search techniques are introduced for efficient rate-distortion optimized motion estimation in a very low bit rate video coding framework. For prediction, three types of predictors are considered: mean, weighted mean, and median. Prediction allows us to constrain the motion vector search to a small diamond-shaped area whose center is the predicted motion vector. The size of the search area is further constrained by employing a probabilistic model. We evaluate two models, both of which permit the contraction or the expansion of the search area as a function of the local statistics of the motion flow. The proposed techniques are analyzed in the context of a very low bit rate DCT-based video coding framework, where a rate-distortion criterion is used for motion estimation as well as for 8/spl times/8 block coding mode selection. A particular resulting very low bit rate video coder is shown experimentally to outperform the H.263 TMN5 simulation model in terms of encoding speed and compression performance, simultaneously.

The MPEG-2 bitstream syntax offers a great variety of coding mode options for encoding a macroblock. We present an MPEG-2 compliant interlaced video encoder that employs an efficient macroblock coding mode selection method. The macroblock coding mode is selected based on a rate-distortion criterion. However, a computational analysis as well as predictive and statistical modeling techniques are introduced that lead to substantially better computation-performance trade-offs. The proposed MPEG-2 video encoder is shown experimentally to be better than the MPEG-2 TM5 encoder in both compression performance and computation requirements, simultaneously.

Predictive rate-distortion (RD) optimized motion estimation techniques are studied and developed for very low bit-rate video coding. Four types of predictors are studied: mean, weighted mean, median, and statistical mean. The weighted mean is obtained using conventional linear prediction techniques. The statistical mean is obtained using a finite-state machine modeling method based on dynamic vector quantization. By employing prediction, the motion vector search can then be constrained to a small area. The effective search area is reduced further by varying its size based on the local statistics of the motion field, through using a Lagrangian as the search matching measure and imposing probabilistic models during the search process. The proposed motion estimation techniques are analyzed within a simple DCT-based video coding framework, where an RD criterion is used for alternating among three coding modes for each 8/spl times/8 block: motion only, motion-compensated prediction and DCT, and intra-DCT. Experimental results indicate that our techniques yield very good computation-performance tradeoffs. When such techniques are applied to an RD optimized H.263 framework at very low bit rates, the resulting H.263 compliant video coder is shown to outperform the H.263 TMN5 coder in terms of compression performance and computations simultaneously.

We introduce a computation-constrained predictive motion estimation algorithm for fast Motion Picture Expert Group-2 (MPEG-2) encoding. Motion estimation is restricted to a relatively small search area whose center is a two-dimensional (2D) median predicted motion vector. The number of computations is further reduced by incorporating explicitly a computational constraint during the estimation process. Experimental results show that the median prediction as well as the dynamic nature of the implementation lead to a dramatic increase in motion estimation speed for only a small loss in compression performance. A unique feature of our algorithm is that the reconstruction quality of the resulting MPEG-2 coder is proportional to the computer's processing power, providing much needed flexibility in many MPEG-2 software applications.

Distortion resulting from ringing and/or aliasing is the most objectionable subjective distortion normally appearing in low bit rate subband/wavelet coded images. This paper presents a method for significantly reducing both ringing and aliasing, while still achieving low bit rates and requiring low encoding/decoding complexity. The method employs a new signal dependent two-band filter bank system that employs short FIR filters, yet results in a relatively small loss of aliasing cancellation in its synthesis section. The signal dependent part of the filter bank is the statistical linear prediction used to decorrelate the subband signals. Experimental results indicate that this approach can achieve very good subjective quality at low bit rates.

We introduce a low-complexity, high-performance DCT-based video coding algorithm that is designed for very low bit rates. The algorithm employs a fast statistical and rate-distortion-optimized motion estimation method with integer-pel accuracy. The searching is constrained to a small diamond-shaped area whose center is the statistical mean, which is obtained using a finite-state machine modeling method based on dynamic state vector quantization. The same modeling method is also employed for coding the motion vectors. A simple rate-distortion criterion is used for alternating between three modes of operation for each 8/spl times/8 block: motion-only coding, motion-compensated predictive coding, and intra coding. Experimental results indicate that the proposed algorithm can outperform current H.263-based video coders significantly at very low bit rates. But, more important is that our algorithm's rate, distortion, and complexity can be easily controlled, a feature desired in many very low bit rate video communication applications due to power and mobility constraints.