With the growth of the Internet and mobile communication industry, multimedia applications form a dominant computer workload. Media workloads are typically executed on Application Specific Integrated Circuits (ASICs), application specific processors (ASPs) or general purpose processors (GPPs). GPPs are flexible and allow changes in the applications and algorithms better than ASICs and ASPs. However, executing these applications on GPPs is done at a high cost. In this paper, we analyze media compression/decompression algorithms from the perspective of the overhead of executing them on a programmable general purpose processor versus ASPs. We choose nine encode/decode programs from audio, image/video and encryption applications. The instruction mix, memory access and parallelism aspects during the execution of these programs are analyzed. Memory access latency is observed to be the main factor influencing the execution time on general purpose processors. Most of these compression/decompression algorithms involve processing the data through execution phases (e.g. quantization, encoding, etc) and temporary results are stored and retrieved between these phases. A metric called overhead memory-access bandwidth per input/output byte is defined to characterize the temporary memory activity of each application. We observe that more than 90% of the memory accesses made by these programs are temporary data stores and loads arising from the general purpose nature of the execution platform. We also study the data parallelism in these applications, indicating the ability of instruction level and data level parallel processors to exploit the parallelism in these applications. The parallelism ranges from 6 to 529 in encode processes and 18 to 558 in decode processes.