Important standards for device-independent color allow many different color encodings. This freedom obliges users of these standards to choose the color space in which to represent their data. A device-independent interchange color space must exhibit an exact mapping to a colorimetric color representation, ability to encode all visible colors, compact representation for given accuracy, and low computational cost for transforms to and from device-dependent spaces. The performance of CIE 1931 XYZ, CIELUV, CIELAB, YES, CCIR 601-2 YCbCr, and SMPTE-C RGB is measured against these requirements. With extensions, all of these spaces can meet the first two requirements. Quantizing error dominates the representational errors of the tested color spaces. Spaces that offer low quantization error also have low gain for image noise. All linear spaces are less compact than nonlinear alternatives. The choice of nonlinearity is not critical; a wide range of gammas yields acceptable results. The choice of primaries for RGB representations is not critical, except that high-chroma primaries should be avoided. Quantizing the components of the candidate spaces with varying precision yields only small improvements. Compatibility with common image data compression techniques leads to the requirement for low luminance contamination, a property that compromises several otherwise acceptable spaces. The conversion of a device-independent representation to popular device spaces by means of trilinear interpolation requires substantially fewer lookup table entries with CCIR 601-2 YCbCr and CIELAB.

The work described in this paper began with a desire to include some linguistic concept of a resource manager within a dataflow language we have been designing [AGP76]. In doing so, we discovered that dataflow monitors (resource managers) provide a natural way of thinking about resources and especially their scheduling. Dataflow semantics are based upon a program composed of asynchronous operators interconnected by lines along which data tokens (messages) flow, such that when all of the input tokens for a given operator have arrived then that operator may fire (execute) by absorbing the input tokens, computing, and producing an output token as its result. These operations closely match one's intuitive model of resource managers (operators) passing signals (tokens) to one another for the purpose of synchronizing and scheduling resource usage. Previously though, dataflow languages [D73, K73, W75] have dealt only with the expression of highly asynchronous yet determinate computations; however, resource management characteristically involves indeterminate computation. The introduction here of dataflow monitors and an explicit nondeterministic merge operator for dataflow streams makes dataflow very well suited for expressing interprocess communication and operations on resources.