Data representation of largest DCT coefficients
Your question is very accurate. Storing the largest  (1 % for instance) coefficients only from a sparsifying transform (DCT, wavelet, else) is fool's-gold, since you (more importantly the decoder) don't you where those are located. 

And storing their binary indices counterbalances the compression gain for the sparsification. So trade-off should be found between theoretical sparsity and storage. For images, in JPEG, a low-frequency to high-frequency parsing scheme (zig-zag) is used. After quantization, lots of zeroed-out coefficients are compacted with End-Of-block symbols.  In JPEG 2000, a multi-scale and bit-wise  path is often  used, and zeroed-out coefficients are compacted with EZM-SPIHT like coding, that perform sort of vector coding of zero bit-plane.

So, some pre-determined way of parsing coefficients, based on prior knowledge on the signal or image structure, is often useful for actual compression, and applicable to specific class of data.