Sentence examples for compression error from inspiring English sources

In this paper, a scheme has been presented which combines compression, error detection, and data encryption.

Based on the empirical equation and prediction results, dynamic self-regulation of compression threshold is realized, and the compression error always stays around a preconfigured value.

As a part of the wavelet method the behaviour of the compression error with respect to different parameters involved in the construction of the diffusion wavelet is tested.

Furthermore, as long as and are set to 0, our scheme achieves a simple switch from the joint compression, error detection, and encryption model to a standard compression model.

In particular, we propose two practical joint schemes: the first one is based on error-correcting randomized arithmetic codes, while the second one employs turbo codes with compression, error protection, and securization capabilities.

Let be e u =u−u d, the difference between the cartoon component before and after compression, i.e., the compression error, and e v =v−v d, then we can rewrite r=f−u d −v d =u+v+ε−u d −v d =ε+e u +e v. Hence, the residual image s computed in step four contains the residual component ε plus the compression errors of the other two components.

Both, data transformation and rendering algorithm, are closely intervened by taking compression errors into account during rendering.

We propose that compression errors are likely to arise from predictive mechanisms that normally maintain spatial constancy across saccades.

The local relaxation algorithm is shown to prevent the accumulation of stretching and compression errors very effectively.

Our technique fusing PCA and DCT enables our framework to: (1) perform balanced compression across all dimensions, (2) control and avoid compression errors and data corruption; and (3) allow more random access to any frame in the data – only a window of frames compressed together is accessed instead of fully decompressing the whole data file.

The researchers, led by Richard Kerensky of the University of Florida, found that, for example, with 16:1 compression, the error rate for the detection of calcification was 30% higher than for uncompressed images.