The JPEG format is the de facto image compression stan- dard, with billions of views every day. Parallelizing the en- tropy decoding step of the JPEG decompression algorithm remains a challenging problem, because codewords are of variable length, and the start-position of a codeword in the bitstream is not known before the previous codeword has been decoded. In this paper, we present JParEnt, a novel parallel entropy decoding method for JPEG decompression on heterogeneous multicores. JParEnt applies a fast block boundary scan on the CPU to determine the start-positions of coefficient blocks in the bitstream, followed by parallel entropy decod- ing on the GPU. Our pipelined execution scheme exploits parallelism between CPU and GPU, and overlaps almost all CPU-to-GPU data transfers with GPU kernel executions. We have evaluated JParEnt's performance for more than 1000 images on four heterogeneous multicore platforms, in- cluding one embedded board. JParEnt is up to 4:3× faster than the SIMD-implementation of the libjpeg-turbo library. On average, JParEnt's CPU-based boundary scan consumes 45% of the sequential entropy decoding time of libjpeg-turbo. Given this new constant for the non-parallelizable part of JPEG decompression, JParEnt achieves up to 97% of the theoretically attainable speedup, with an average of 95%.