Video streams in general do not adapt to changes in the network environment, causing image quality to suffer. Graceful video scaling requires fine granular adaption, and scalable video codecs like SPEG and MPEG-4 FGS provide this ability. Little research has been done regarding how continuous quality changes affect perceived video quality, and how existing metrics can be used to measure this. There is no objective metric that addresses this particular problem. Some objective metrics emulate the human visual system, and the project compared those with subjective results. The comparison indicated objective tests could be used in place of subjective tests, which are more resource expensive. More testing was needed to draw any conclusions about this topic. Results from the quality evaluation tests could be used to generate a utility function for measuring perceived quality in scalable video. It would be based on exponential functions describing each test parameter. Due to time constraints, only an abstract method to approach this problem was proposed. Some shortcomings were observed. The test parameters were not numerous enough, and their values were not distanced far enough apart. Testing performed with the chosen subjective evaluation method, DSCQS, resulted in a data set that was too small for serious usage. The test should have had access to more participants, and the viewers should have seen fewer clips but with more parameter variations.